This document discusses and analyzes six major data mining algorithms: C4.5, k-Means, SVM, Apriori, EM, and PageRank. It provides a brief description of each algorithm, discusses their impact, and reviews current and future research on each one. These six algorithms cover important data mining topics like classification, clustering, statistical learning, association analysis, and link mining.
Simulating Multivariate Random Normal Data using Statistical Computing Platfo...ijtsrd
This document describes how to simulate multivariate normal random data using the statistical computing platform R. It discusses two main decomposition methods for generating such data - Eigen decomposition and Cholesky decomposition. These methods decompose the variance-covariance matrix in different ways to simulate random normal values that match the desired mean and variance-covariance structure. The document provides code examples in R to implement both methods and compares the results. It finds that both methods accurately reproduce the target multivariate normal distribution.
Data Warehouses are structures with large amount of data collected from heterogeneous sources to be
used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected
which analysis requires great memory and computation cost. Data reduction methods were proposed to
make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA)
as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods
to conduct this reduction. Our approach identifies reduced subset of dimensions p’ from the initial subset p
where p'<p where it is proposed to find the profile fact that is the closest to reference. Gas identify the
possible subsets and the Khi² formula of the ACM evaluates the quality of each subset. The study is based
on a distance measurement between the reference and n facts profile extracted from the warehouse.
The document summarizes five papers that address challenges in context-aware recommendation systems using factorization methods. Three key challenges are high dimensionality, data sparsity, and cold starts. The papers propose various algorithms using matrix factorization and tensor factorization to address these challenges. COT models each context as an operation on user-item pairs to reduce dimensionality. Another approach extracts latent contexts from sensor data using deep learning and matrix factorization. CSLIM extends the SLIM algorithm to incorporate contextual ratings. TAPER uses tensor factorization to integrate various contexts for expert recommendations. Finally, GFF provides a generalized factorization framework to handle different recommendation models. The document analyzes how well each paper meets the challenges.
This document discusses using MapReduce to calculate rough set approximations in parallel for big data. It begins with an introduction to rough sets and how they are calculated based on lower and upper approximations. It then discusses related work applying rough sets and MapReduce to large datasets. The document proposes a parallel method for computing rough set approximations using MapReduce by parallelizing the computation of equivalence classes, decision classes, and their associations. This allows rough set approximations to be calculated more efficiently for big data as compared to traditional serial methods. The document concludes that MapReduce provides an effective framework for the parallel rough set calculations.
- The document provides information about a course on multivariate data analysis, including the lecturer, timetable, textbooks, and course contents.
- The course will cover topics such as data analysis basics, principal components analysis, factor analysis, discriminant analysis, cluster analysis, and canonical correlation analysis.
- Assessment will be based on the syllabus. Textbooks, online tools, lecture slides, and data files will provide additional resources.
The D-basis Algorithm for Association Rules of High ConfidenceITIIIndustries
We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data
Simulating Multivariate Random Normal Data using Statistical Computing Platfo...ijtsrd
This document describes how to simulate multivariate normal random data using the statistical computing platform R. It discusses two main decomposition methods for generating such data - Eigen decomposition and Cholesky decomposition. These methods decompose the variance-covariance matrix in different ways to simulate random normal values that match the desired mean and variance-covariance structure. The document provides code examples in R to implement both methods and compares the results. It finds that both methods accurately reproduce the target multivariate normal distribution.
Data Warehouses are structures with large amount of data collected from heterogeneous sources to be
used in a decision support system. Data Warehouses analysis identifies hidden patterns initially unexpected
which analysis requires great memory and computation cost. Data reduction methods were proposed to
make this analysis easier. In this paper, we present a hybrid approach based on Genetic Algorithms (GA)
as Evolutionary Algorithms and the Multiple Correspondence Analysis (MCA) as Analysis Factor Methods
to conduct this reduction. Our approach identifies reduced subset of dimensions p’ from the initial subset p
where p'<p where it is proposed to find the profile fact that is the closest to reference. Gas identify the
possible subsets and the Khi² formula of the ACM evaluates the quality of each subset. The study is based
on a distance measurement between the reference and n facts profile extracted from the warehouse.
The document summarizes five papers that address challenges in context-aware recommendation systems using factorization methods. Three key challenges are high dimensionality, data sparsity, and cold starts. The papers propose various algorithms using matrix factorization and tensor factorization to address these challenges. COT models each context as an operation on user-item pairs to reduce dimensionality. Another approach extracts latent contexts from sensor data using deep learning and matrix factorization. CSLIM extends the SLIM algorithm to incorporate contextual ratings. TAPER uses tensor factorization to integrate various contexts for expert recommendations. Finally, GFF provides a generalized factorization framework to handle different recommendation models. The document analyzes how well each paper meets the challenges.
This document discusses using MapReduce to calculate rough set approximations in parallel for big data. It begins with an introduction to rough sets and how they are calculated based on lower and upper approximations. It then discusses related work applying rough sets and MapReduce to large datasets. The document proposes a parallel method for computing rough set approximations using MapReduce by parallelizing the computation of equivalence classes, decision classes, and their associations. This allows rough set approximations to be calculated more efficiently for big data as compared to traditional serial methods. The document concludes that MapReduce provides an effective framework for the parallel rough set calculations.
- The document provides information about a course on multivariate data analysis, including the lecturer, timetable, textbooks, and course contents.
- The course will cover topics such as data analysis basics, principal components analysis, factor analysis, discriminant analysis, cluster analysis, and canonical correlation analysis.
- Assessment will be based on the syllabus. Textbooks, online tools, lecture slides, and data files will provide additional resources.
The D-basis Algorithm for Association Rules of High ConfidenceITIIIndustries
We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data
Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
This study uses cluster analysis and k-means algorithms to analyze student data and group students according to their characteristics. The data was first prepared by joining relevant tables and correcting errors. Then data selection and transformation was performed to determine fields for analysis. The k-means algorithm was applied and successfully partitioned students into 5 clusters based on university entrance exam percentages and grades. Cluster 1 had the most successful students while Cluster 4 had the least successful. Presentation of the results showed Cluster 1 was mainly composed of Arts/Sciences students while Cluster 4 was mostly Communications/Business students. The study demonstrates how data mining techniques can provide valuable insights when applied to education data.
Analytical Hierarchical Process has been used as a useful methodology for multi-criteria decision making environments with substantial applications in recent years. But the weakness of the traditional AHP method lies in the use of subjective judgement based assessment and standardized scale for pairwise comparison matrix creation. The paper proposes a Condorcet Voting Theory based AHP method to solve multi criteria decision making problems where Analytical Hierarchy Process (AHP) is combined with Condorcet theory based preferential voting technique followed by a quantitative ratio method for framing the comparison matrix instead of the standard importance scale in traditional AHP approach. The consistency ratio (CR) is calculated for both the approaches to determine and compare the consistency of both the methods. The results reveal Condorcet- AHP method to be superior generating lower consistency ratio and more accurate ranking of the criterion for solving MCDM problems.
This document provides an overview of exploratory data analysis (EDA). It discusses EDA objectives like understanding data, uncovering structure, and developing models. Key EDA techniques covered include bivariate correlation analysis using scatter plots and correlation coefficients. Scatter plots graphically show the relationship between two variables, while the Pearson's correlation coefficient (r) numerically measures the strength and direction of linear relationships between -1 and 1. The document also introduces multivariate correlation analysis and principal component analysis as additional EDA tools.
The document provides an introduction to decision trees and CART (Classification and Regression Trees) analysis. It discusses how CART can automatically select relevant predictors, handle outliers and missing values, and produce simple and interpretable models. It describes the history and development of CART, provides an example of how a CART model is built to predict heart attack survival, and outlines the key stages and processes involved in growing, pruning and selecting optimal decision trees.
This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.
This document discusses various techniques for document classification in text mining, including k-nearest neighbor algorithms, decision trees, and naive Bayes classifiers. It covers how each technique works, their advantages and drawbacks, how to evaluate classifier performance, and examples of applications for document classification.
This document discusses Chi-Square tests. It begins with an overview of Chi-Square, noting that it is a non-parametric test where the test statistic follows a Chi-Square distribution. It then discusses the characteristics of Chi-Square tests, including that they are distribution free and easy to calculate. Several common uses of Chi-Square tests are provided, such as testing goodness of fit and independence. The document then separates into two phases - the first discussing theory and the second providing examples. Phase one delves further into the equation, level of significance, and degrees of freedom. Phase two demonstrates steps for performing a Chi-Square test using observed and expected values.
The document discusses cluster analysis, an unsupervised machine learning technique used to group similar cases together. It describes how cluster analysis is used in marketing research for market segmentation, understanding customer behaviors, and identifying new product opportunities. The key steps in cluster analysis involve selecting a distance measure, clustering algorithm, determining the optimal number of clusters, and validating the results.
Data management 26 sept 2020 by dr tmh myanmarTin Myo Han
The document discusses qualitative and quantitative data analysis. It provides examples of different types of variables like nominal, ordinal and continuous numerical variables. Different statistical tests are described for different types of variables, including chi-square tests for categorical variables, t-tests for continuous outcome variables, ANOVA for comparing multiple groups, correlation for continuous variables, and regression for analyzing relationships between variables. Examples of properly collecting, entering, checking, and analyzing data are also provided.
This document provides an overview of statistics concepts and tasks. It includes 5 tasks covering topics like data collection methods, graphing data, measures of central tendency, and variance. The document also defines key statistical terms and graphs. It aims to introduce students to fundamental statistical concepts and how statistics are used across various domains like weather, health, business and more.
This chapter discusses getting to know your data through descriptive analysis. It covers data objects and attribute types, including nominal, binary, numeric, discrete and continuous attributes. Statistical descriptions like mean, median, mode, variance and standard deviation are explained. Visualization techniques explored include histograms, boxplots, quantile plots, scatter plots and Chernoff faces. Dimensionality, sparsity, distribution and other data characteristics are also covered.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results
The science behind predictive analytics a text mining perspectiveankurpandeyinfo
This document summarizes techniques for predictive analytics using text mining and unstructured text data. It discusses representing text data using bag-of-words models and vector space models. Dimensionality reduction techniques like latent semantic analysis and topic models like latent Dirichlet allocation are described for extracting semantic information from text. Clustering methods like k-means clustering and hierarchical clustering are discussed for grouping similar documents. The document also covers classification techniques like rule-based classifiers, decision trees, and linear classifiers like logistic regression for assigning labels to documents.
This document discusses recommendation systems and topic modeling for documents using machine learning techniques. It begins by introducing recommendation systems and different types of recommendation literature, including item similarity, collaborative filtering, and hierarchical models. It then discusses bringing in user choice data and different collaborative filtering approaches like k-nearest neighbor prediction and matrix factorization. The document also covers topic modeling, including latent Dirichlet allocation, and how topic models can be combined with user choice models. It concludes by discussing challenges in causal inference when using machine learning.
This document provides an overview and agenda for a presentation on multivariate analysis. It introduces the presenter, Dr. Nisha Arora, and lists her qualifications and areas of expertise, which include statistics, data analysis, machine learning, and online teaching. The presentation agenda covers topics like cluster analysis using SPSS, including different clustering algorithms, applications of cluster analysis, and how to interpret and validate clustering outputs and solutions.
On Tracking Behavior of Streaming Data: An Unsupervised ApproachWaqas Tariq
In the recent years, data streams have been in the gravity of focus of quite a lot number of researchers in different domains. All these researchers share the same difficulty when discovering unknown pattern within data streams that is concept change. The notion of concept change refers to the places where underlying distribution of data changes from time to time. There have been proposed different methods to detect changes in the data stream but most of them are based on an unrealistic assumption of having data labels available to the learning algorithms. Nonetheless, in the real world problems labels of streaming data are rarely available. This is the main reason why data stream communities have recently focused on unsupervised domain. This study is based on the observation that unsupervised approaches for learning data stream are not yet matured; namely, they merely provide mediocre performance specially when applied on multi-dimensional data streams. In this paper, we propose a method for Tracking Changes in the behavior of instances using Cumulative Density Function; abbreviated as TrackChCDF. Our method is able to detect change points along unlabeled data stream accurately and also is able to determine the trend of data called closing or opening. The advantages of our approach are three folds. First, it is able to detect change points accurately. Second, it works well in multi-dimensional data stream, and the last but not the least, it can determine the type of change, namely closing or opening of instances over the time which has vast applications in different fields such as economy, stock market, and medical diagnosis. We compare our algorithm to the state-of-the-art method for concept change detection in data streams and the obtained results are very promising.
Estonia has a population of 1.4 million people with high adoption rates of technology - over 100% use mobile phones, 62% use computers, and 61% use the internet. The Estonian ICT market was worth over 1 billion Euros in 2005, accounting for around 9.5% of GDP. Broadband connectivity is widespread, with over 230,000 connections and many service providers offering packages for around 20 Euros per month. The ITL Association represents over 40 ICT and telecommunications companies in Estonia with combined revenues of 850 million Euros in 2005.
An Oktoberfest celebration will be taking place during the third annual Jacksonville Art Walk on October 7th from 5-10 PM across more than 15 blocks downtown. Attendees can enjoy German beers, sausages, and a sausage eating contest hosted by Hoptinger Bier Garden. There will also be over a dozen live music performances, some with a traditional German style. Former Jaguars mascot Jaxson de Ville will host the event. Art galleries and handmade goods will also be available during the regular Art Walk activities. Over 15,000 people attended last year's Oktoberfest and even more are expected this year.
Lessons to be Learnt in Life's Journey.DocVedvrat Alok
1. The document discusses the purpose of life and lessons to be learned from life's journey. It explores why souls incarnate on Earth, noting we are sent to learn lessons, shed negativity, and perform duties for the divine.
2. Souls incarnate to gain balance, do deeds for the divine, learn coexistence, realize self-limits versus the perfect divine, shed negativity with courage and humility, learn love as our original state, and find true happiness through positive living.
3. Life is a test and journey that helps souls grow in awareness of their divine nature and excel in material realms before returning to their original blissful source.
Getting the Most Out of LinkedIn outlines 7 benefits of using LinkedIn including making connections, identifying networking opportunities, and leveraging your personal brand. It provides tips for developing an effective profile such as writing a compelling headline and summary, adding recommendations, and using keywords and groups to get found. The document emphasizes engaging with your network by posting content, participating in discussions, and connecting or introducing others in order to build relationships and prospects on LinkedIn.
Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
This study uses cluster analysis and k-means algorithms to analyze student data and group students according to their characteristics. The data was first prepared by joining relevant tables and correcting errors. Then data selection and transformation was performed to determine fields for analysis. The k-means algorithm was applied and successfully partitioned students into 5 clusters based on university entrance exam percentages and grades. Cluster 1 had the most successful students while Cluster 4 had the least successful. Presentation of the results showed Cluster 1 was mainly composed of Arts/Sciences students while Cluster 4 was mostly Communications/Business students. The study demonstrates how data mining techniques can provide valuable insights when applied to education data.
Analytical Hierarchical Process has been used as a useful methodology for multi-criteria decision making environments with substantial applications in recent years. But the weakness of the traditional AHP method lies in the use of subjective judgement based assessment and standardized scale for pairwise comparison matrix creation. The paper proposes a Condorcet Voting Theory based AHP method to solve multi criteria decision making problems where Analytical Hierarchy Process (AHP) is combined with Condorcet theory based preferential voting technique followed by a quantitative ratio method for framing the comparison matrix instead of the standard importance scale in traditional AHP approach. The consistency ratio (CR) is calculated for both the approaches to determine and compare the consistency of both the methods. The results reveal Condorcet- AHP method to be superior generating lower consistency ratio and more accurate ranking of the criterion for solving MCDM problems.
This document provides an overview of exploratory data analysis (EDA). It discusses EDA objectives like understanding data, uncovering structure, and developing models. Key EDA techniques covered include bivariate correlation analysis using scatter plots and correlation coefficients. Scatter plots graphically show the relationship between two variables, while the Pearson's correlation coefficient (r) numerically measures the strength and direction of linear relationships between -1 and 1. The document also introduces multivariate correlation analysis and principal component analysis as additional EDA tools.
The document provides an introduction to decision trees and CART (Classification and Regression Trees) analysis. It discusses how CART can automatically select relevant predictors, handle outliers and missing values, and produce simple and interpretable models. It describes the history and development of CART, provides an example of how a CART model is built to predict heart attack survival, and outlines the key stages and processes involved in growing, pruning and selecting optimal decision trees.
This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.
This document discusses various techniques for document classification in text mining, including k-nearest neighbor algorithms, decision trees, and naive Bayes classifiers. It covers how each technique works, their advantages and drawbacks, how to evaluate classifier performance, and examples of applications for document classification.
This document discusses Chi-Square tests. It begins with an overview of Chi-Square, noting that it is a non-parametric test where the test statistic follows a Chi-Square distribution. It then discusses the characteristics of Chi-Square tests, including that they are distribution free and easy to calculate. Several common uses of Chi-Square tests are provided, such as testing goodness of fit and independence. The document then separates into two phases - the first discussing theory and the second providing examples. Phase one delves further into the equation, level of significance, and degrees of freedom. Phase two demonstrates steps for performing a Chi-Square test using observed and expected values.
The document discusses cluster analysis, an unsupervised machine learning technique used to group similar cases together. It describes how cluster analysis is used in marketing research for market segmentation, understanding customer behaviors, and identifying new product opportunities. The key steps in cluster analysis involve selecting a distance measure, clustering algorithm, determining the optimal number of clusters, and validating the results.
Data management 26 sept 2020 by dr tmh myanmarTin Myo Han
The document discusses qualitative and quantitative data analysis. It provides examples of different types of variables like nominal, ordinal and continuous numerical variables. Different statistical tests are described for different types of variables, including chi-square tests for categorical variables, t-tests for continuous outcome variables, ANOVA for comparing multiple groups, correlation for continuous variables, and regression for analyzing relationships between variables. Examples of properly collecting, entering, checking, and analyzing data are also provided.
This document provides an overview of statistics concepts and tasks. It includes 5 tasks covering topics like data collection methods, graphing data, measures of central tendency, and variance. The document also defines key statistical terms and graphs. It aims to introduce students to fundamental statistical concepts and how statistics are used across various domains like weather, health, business and more.
This chapter discusses getting to know your data through descriptive analysis. It covers data objects and attribute types, including nominal, binary, numeric, discrete and continuous attributes. Statistical descriptions like mean, median, mode, variance and standard deviation are explained. Visualization techniques explored include histograms, boxplots, quantile plots, scatter plots and Chernoff faces. Dimensionality, sparsity, distribution and other data characteristics are also covered.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results
The science behind predictive analytics a text mining perspectiveankurpandeyinfo
This document summarizes techniques for predictive analytics using text mining and unstructured text data. It discusses representing text data using bag-of-words models and vector space models. Dimensionality reduction techniques like latent semantic analysis and topic models like latent Dirichlet allocation are described for extracting semantic information from text. Clustering methods like k-means clustering and hierarchical clustering are discussed for grouping similar documents. The document also covers classification techniques like rule-based classifiers, decision trees, and linear classifiers like logistic regression for assigning labels to documents.
This document discusses recommendation systems and topic modeling for documents using machine learning techniques. It begins by introducing recommendation systems and different types of recommendation literature, including item similarity, collaborative filtering, and hierarchical models. It then discusses bringing in user choice data and different collaborative filtering approaches like k-nearest neighbor prediction and matrix factorization. The document also covers topic modeling, including latent Dirichlet allocation, and how topic models can be combined with user choice models. It concludes by discussing challenges in causal inference when using machine learning.
This document provides an overview and agenda for a presentation on multivariate analysis. It introduces the presenter, Dr. Nisha Arora, and lists her qualifications and areas of expertise, which include statistics, data analysis, machine learning, and online teaching. The presentation agenda covers topics like cluster analysis using SPSS, including different clustering algorithms, applications of cluster analysis, and how to interpret and validate clustering outputs and solutions.
On Tracking Behavior of Streaming Data: An Unsupervised ApproachWaqas Tariq
In the recent years, data streams have been in the gravity of focus of quite a lot number of researchers in different domains. All these researchers share the same difficulty when discovering unknown pattern within data streams that is concept change. The notion of concept change refers to the places where underlying distribution of data changes from time to time. There have been proposed different methods to detect changes in the data stream but most of them are based on an unrealistic assumption of having data labels available to the learning algorithms. Nonetheless, in the real world problems labels of streaming data are rarely available. This is the main reason why data stream communities have recently focused on unsupervised domain. This study is based on the observation that unsupervised approaches for learning data stream are not yet matured; namely, they merely provide mediocre performance specially when applied on multi-dimensional data streams. In this paper, we propose a method for Tracking Changes in the behavior of instances using Cumulative Density Function; abbreviated as TrackChCDF. Our method is able to detect change points along unlabeled data stream accurately and also is able to determine the trend of data called closing or opening. The advantages of our approach are three folds. First, it is able to detect change points accurately. Second, it works well in multi-dimensional data stream, and the last but not the least, it can determine the type of change, namely closing or opening of instances over the time which has vast applications in different fields such as economy, stock market, and medical diagnosis. We compare our algorithm to the state-of-the-art method for concept change detection in data streams and the obtained results are very promising.
Estonia has a population of 1.4 million people with high adoption rates of technology - over 100% use mobile phones, 62% use computers, and 61% use the internet. The Estonian ICT market was worth over 1 billion Euros in 2005, accounting for around 9.5% of GDP. Broadband connectivity is widespread, with over 230,000 connections and many service providers offering packages for around 20 Euros per month. The ITL Association represents over 40 ICT and telecommunications companies in Estonia with combined revenues of 850 million Euros in 2005.
An Oktoberfest celebration will be taking place during the third annual Jacksonville Art Walk on October 7th from 5-10 PM across more than 15 blocks downtown. Attendees can enjoy German beers, sausages, and a sausage eating contest hosted by Hoptinger Bier Garden. There will also be over a dozen live music performances, some with a traditional German style. Former Jaguars mascot Jaxson de Ville will host the event. Art galleries and handmade goods will also be available during the regular Art Walk activities. Over 15,000 people attended last year's Oktoberfest and even more are expected this year.
Lessons to be Learnt in Life's Journey.DocVedvrat Alok
1. The document discusses the purpose of life and lessons to be learned from life's journey. It explores why souls incarnate on Earth, noting we are sent to learn lessons, shed negativity, and perform duties for the divine.
2. Souls incarnate to gain balance, do deeds for the divine, learn coexistence, realize self-limits versus the perfect divine, shed negativity with courage and humility, learn love as our original state, and find true happiness through positive living.
3. Life is a test and journey that helps souls grow in awareness of their divine nature and excel in material realms before returning to their original blissful source.
Getting the Most Out of LinkedIn outlines 7 benefits of using LinkedIn including making connections, identifying networking opportunities, and leveraging your personal brand. It provides tips for developing an effective profile such as writing a compelling headline and summary, adding recommendations, and using keywords and groups to get found. The document emphasizes engaging with your network by posting content, participating in discussions, and connecting or introducing others in order to build relationships and prospects on LinkedIn.
Internet forums allow users to have online discussions through posting messages on a message board. Forums are organized hierarchically with categories, subforums, and threads. They differ from chat rooms in that messages are longer and archived. Forums have features like private messages, attachments, emoticons, and user groups with varying privileges. Common forum topics include questions, comparisons, debates, and polls. Forums are moderated and administered to keep discussions civil and the site running smoothly.
This document is a 17-page mathematics assignment from a student at the State Polytechnic of Bangka Belitung. It covers differential equations in two chapters - the first chapter discusses differential equations and the second chapter applies differential equations. The student's name is Pradana Prisma S and they are studying electronics engineering in their third semester.
yakov perelman Problemas y experimentos recreativosJosé García
El documento presenta a Yakov Perelman, un pedagogo y popularizador de la ciencia ruso que escribió varios libros recreativos sobre física, matemáticas y astronomía. Explica que en 1913 publicó su libro más popular "Física Recreativa", el cual enseñaba a los lectores a pensar científicamente y resolver problemas de la vida cotidiana. También describe algunos rompecabezas y experimentos simples con papel que Perelman usaba para estimular la curiosidad científica en los niños.
Este curso de 15 horas tiene como objetivo principal enseñar a elaborar cosméticos de forma artesanal utilizando productos naturales. Los objetivos específicos incluyen conocer las necesidades de cada tipo de piel y los ingredientes más adecuados para diferentes tipos de cosméticos, como limpiadores, cremas antiarrugas y tratamientos corporales. El curso está dirigido a personas interesadas en hacer sus propios cosméticos de forma natural en casa y cubre temas como ingredientes naturales, el tipo de piel, recetas para diferentes tip
This document provides an overview and guide to the retail leasing process. It begins with an introduction to Colliers International, an award-winning team of retail leasing professionals. The guide then covers topics such as making educated decisions about business space needs, signage best practices, the steps in the retail leasing process, current retail leasing trends, ways to reduce customer wait times, and a glossary of common leasing terms. The goal is to reflect Colliers International's expertise in retail leasing and provide information to help clients optimize their retail space.
The document provides information about Menara Mesiniaga, the IBM headquarters building in Subang Jaya, Malaysia. It was designed by architect Ken Yeang and completed in 1992. The building uses bioclimatic design principles to be environmentally friendly. It has 14 floors and a distinctive exterior design with terraced garden balconies and external louvers for shade. The building stands out from the surrounding context of low-rise buildings and uses its prominent location on a highway as a showcase for IBM's technology.
Un blog es una página web en la que se publican regularmente artículos cortos llamados "posts" sobre temas específicos. Los posts suelen incluir fotografías, videos y gráficos que ilustran el tema. Los blogs comenzaron como espacios para que las personas expresaran sus opiniones y compartieran contenido, y aunque la mayoría son escritos por individuos, algunos son creados y escritos por grupos.
The document summarizes 10 influential data mining algorithms:
1. C4.5 decision tree algorithm and its successor C5.0, which can construct classifiers as decision trees or rulesets.
2. K-means clustering algorithm, an iterative algorithm that partitions data into k clusters based on minimizing distances between data points and cluster centers.
3. Additional algorithms covered include SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These algorithms cover important data mining tasks such as classification, clustering, association analysis, and link mining.
The document summarizes 10 influential data mining algorithms:
1. C4.5 and its successor C5.0 are decision tree algorithms that use information gain to build trees and pruning to avoid overfitting. C5.0 adds boosting and improved scalability.
2. K-means is a simple iterative clustering algorithm that partitions data into k clusters by minimizing distances between data points and cluster centers.
3. The top 10 algorithms were identified through a nomination and voting process involving data mining experts and cover important areas like classification, clustering, association analysis, and link mining.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. Evaluation metrics like precision, recall, F1-score, and accuracy are used to evaluate and compare model performance on test data.
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. It preprocesses the dataset, trains models on a training set, and evaluates them using metrics like precision, recall, and F1-score calculated from the confusion matrix on a test set.
Perfomance Comparison of Decsion Tree Algorithms to Findout the Reason for St...ijcnes
Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, rule mining can be applied on the educational data for predicting the students behavior. This paper focuses on finding thesuitablealgorithm which yields the best result to find out the reason behind students absenteeism in an academic year. The first step in this processis to gather students data by using questionnaire.The datais collected from 123 under graduate students from a private college which is situated in a semirural area. The second step is to clean the data which is appropriate for mining purpose and choose the relevant attributes. In the final step, three different Decision tree induction algorithms namely, ID3(Iterative Dichotomiser), C4.5 and CART(Classification and Regression Tree)were applied for comparison of results for the same data sample collected using questionnaire. The results were compared to find the algorithm which yields the best result in predicting the reason for student s absenteeism.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document discusses using particle swarm optimization to improve the k-prototype clustering algorithm. The k-prototype algorithm clusters data with both numeric and categorical attributes but can get stuck in local optima. The proposed method uses particle swarm optimization, a global optimization technique, to guide the k-prototype algorithm towards better clusterings. Particle swarm optimization models potential solutions as particles that explore the search space. It is integrated with k-prototype clustering to avoid locally optimal solutions and produce better clusterings. The method is tested on standard benchmark datasets and shown to outperform traditional k-modes and k-prototype clustering algorithms.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting, model evaluation methods, and comparing model performance.
This document discusses data mining classification and decision trees. It defines classification, provides examples, and discusses techniques like decision trees. It covers decision tree induction processes like determining the best split, measures of impurity, and stopping criteria. It also addresses issues like overfitting and model evaluation, discussing metrics, methods of evaluation like cross validation, and comparing models.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
Survey on Various Classification Techniques in Data Miningijsrd.com
Dynamic Classification is an information mining (machine learning) strategy used to anticipate bunch participation for information cases. In this paper, we show the essential arrangement systems. A few significant sorts of arrangement technique including induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The objective of this review is to give a complete audit of distinctive characterization procedures in information mining.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION O...cscpconf
Classification is a step by step practice for allocating a given piece of input into any of the given
category. Classification is an essential Machine Learning technique. There are many
classification problem occurs in different application areas and need to be solved. Different
types are classification algorithms like memory-based, tree-based, rule-based, etc are widely
used. This work studies the performance of different memory based classifiers for classification
of Multivariate data set from UCI machine learning repository using the open source machine
learning tool. A comparison of different memory based classifiers used and a practical
guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed
Review of Algorithms for Crime Analysis & PredictionIRJET Journal
This document reviews algorithms that can be used for crime analysis and prediction. It discusses various data mining and machine learning techniques including classification algorithms like decision trees, k-nearest neighbors, and random forests as well as clustering algorithms like k-means clustering. Deep learning techniques are also examined for identifying relationships between different types of crimes and predicting where and when crimes may occur. The document evaluates these different algorithmic approaches and concludes that major developments in data science and machine learning now allow for effective crime analysis and prediction by discovering patterns in criminal data.
The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
A Formal Machine Learning or Multi Objective Decision Making System for Deter...Editor IJCATR
Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network.
This document describes a student comprehensive evaluation system based on data mining methods. The system constructs a student data warehouse containing information on student performance, course selections, and employment outcomes. It then builds a multi-dimensional OLAP model and uses an ID3 classification algorithm to construct a decision tree that can evaluate students' comprehensive quality based on their moral, academic, and extracurricular activity levels. A prototype of the system was developed with interfaces for searching student records and viewing evaluation results. The system aims to provide useful information to help improve student management and guide employment.
Similar to An Algorithm Analysis on Data Mining-396 (20)
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An Algorithm Analysis on Data Mining
Nida Rashid
Abstract: This paper displays the main 6 information mining calculations distinguished by the IEEE Global
Conference on Data Mining (ICDM: C4.5, k-Means, SVM, Apriori, EM, Page Rank. These main 6 calculations are
among the most persuasive information mining calculations in the examination community. With each calculation,
we give a depiction of the calculation, examine the effect of the calculation, and review current and further
research on the calculation. These 6 calculations spread grouping, bunching, measurable learning, affiliation
examination, and connection mining, which are all among the most vital subjects in information mining innovative
work.
Keywords: Data mining, K-Means, Apriori.
I. INTRODUCTION
With an end goal to distinguish the absolute most powerful calculations that have been generally utilized in the
information mining group, the IEEE International Conference on Data Mining (ICDM, http://www.cs.uvm.edu/~icdm/)
recognized the main 6 calculations in information digging for presentation at ICDM '06 in Hong Kong. As the initial
phase in the recognizable proof procedure, in September 2006 we welcomed the ACMKDD Advancement Award and
IEEE ICDM Research Contributions Award victors to each designate up to 6 best-known calculations in information
mining. All aside from one in this recognized set of honor victors reacted to our welcome. We requested that every
selection give the taking after data: (a) the calculation name,
(b) A brief avocation, and
(c) An agent distribution reference.
We likewise exhorted that each selected calculation ought to have been generally referred to and utilized by different
scientists as a part of the field, and the selections from every nominator as a gathering ought to have a sensible
representation of the diverse territories in information mining. After the selections in Step 1, we confirmed every selection
for its references on Google Researcher in late October 2006, and evacuated those assignments that did not have no less
than 50 references. All remaining assignments were then sorted out in 6 subjects: affiliation investigation, order,
bunching, measurable learning, stowing and boosting, consecutive examples, incorporated mining, unpleasant sets, link
mining, and chart mining. For some of these 18 calculations for example, k-implies, the delegate distribution was not so
much the first paper that presented the calculation, yet a late paper that highlights the significance of the system. These
agent productions are accessible at the ICDM site (http://www.cs.uvm. edu/~icdm/calculations/CandidateList.shtml). In
the third stride of the ID process, we had a more extensive contribution of the exploration group. We welcomed the
Program Committee individuals from KDD-06 (the 2006 ACM SIGKDD Worldwide Conference on Knowledge
Discovery and Data Mining), ICDM '06 (the 2006 IEEE International Conference on Data Mining), and SDM '06 (the
2006 SIAM International Gathering on Data Mining), and additionally the ACMKDD Innovation Award and IEEE ICDM
Research Contributions Award victors to every vote in favor of up to 6 no doubt understood calculations from the 18-
calculation applicant rundown. The voting consequences of this stride were displayed at the ICDM '06 board on Top 6
Algorithms in Data Mining. At the ICDM '06 board of December 21, 2006, we likewise brought an open vote with every
one of the 145 participants on the main 6 calculations from the over 18-calculation competitor rundown, and the main 6
calculations from this open vote were the same as the voting results from the above third step. The 3-hour board was
composed as the last session of the ICDM '06 gathering, in parallel with 7 paper presentation sessions of the Web
Intelligence (WI '06) and Intelligent Agent Innovation (IAT '06) meetings at the same area, and pulling in 145 members to
this board plainly demonstrated that the board was an awesome achievement.
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2. C4.5 AND PAST
2.1 Introduction
Frameworks that develop classifiers are one of the usually utilized instruments as a part of information mining. Such
frameworks take as information an accumulation of cases, every having a place with one of a little number of classes and
depicted by its values for a settled arrangement of traits, and yield a classifier that can precisely foresee the class to which
another case has a place. These notes depict C4.5 [64], a relative of CLS [41] and ID3 [62]. Like CLS and ID3, C4.5
produces classifiers communicated as choice trees, however it can likewise develop classifiers in more understandable
ruleset structure. We will layout the calculations utilized in C4.5, highlight a few adjustments in its successor See5/C5.0,
and finish up with two or three open examination issues.
2.2 Decision trees
Given a set S of cases, C4.5 first grows a beginning tree utilizing the separation and- vanquish calculation as takes after:
If all the cases in S have a place with the same class or S is little, the tree is a leaf named with the most continuous class in
S.
Otherwise, pick a test in view of a solitary characteristic with two or more results. Make this test the foundation of the tree
with one branch for every result of the test, allotment S into relating subsets S1, S2, . . .as per the result for every case,
and apply the same system recursively to every sub.
There are generally numerous tests that could be picked in this last step. C4.5 utilizes two heuristic criteria to rank
conceivable tests: data pick up, which minimizes the aggregate entropy of the subsets {Si } (yet is intensely one-sided
towards tests with various results), and the default pick up proportion that partitions data pick up by the data gave by the
test results. Qualities can be either numeric or ostensible and this decides the organization of the test results. For a
numeric quality A they are {A ≤ h, A > h} where the limit h is found by sorting S on the estimations of An and picking
the part between progressive values that amplifies the standard above. A quality A with discrete qualities has of course
one result for every quality, except a choice permits the qualities to be gathered into two or more subsets with one result
for every subset. The starting tree is then pruned to abstain from over fitting. The pruning calculation is in view of a
cynical assessment of the slip rate connected with an arrangement of N cases, E of which don't fit in with the most
successive class. Rather than E/N, C4.5 decides the furthest reaches of the binomial likelihood when E occasions have
been seen in N trials, utilizing a client determined certainty whose default quality is 0.25. Pruning is completed from the
leaves to the root. The assessed lapse at a leaf with N cases and E mistakes is N times the critical lapse rate as above. For
a sub tree, C4.5 includes the evaluated mistakes of the branches and contrasts this with the assessed slip if the sub tree is
supplanted by a leaf; if the last is no higher than the previous, the sub tree is pruned. Likewise, C4.5 checks the evaluated
blunder if the sub tree is supplanted by one of its branches and when this seems gainful the tree is adjusted in like manner.
The pruning procedure is finished in one go through the tree. C4.5's tree-development calculation contrasts in a few
regards from CART [9], for example:
• Tests in CART are constantly two fold, however C4.5 permits two or more results.
• CART utilizes the Gini assorted qualities file to rank tests, while C4.5 utilizes data based Criteria.
• CART prunes trees utilizing an expense intricacy display whose parameters are assessed by cross-acceptance; C4.5
utilizes a solitary pass calculation got from binomial certainty limits.
• This brief exchange has not specified what happens when some of a case's qualities are obscure. Truck searches for
surrogate tests that surmised the results when they tried quality has an obscure worth, however C4.5 distributes the case
probabilistically among the outputs.
2.3 Rule set classifiers
Complex choice trees can be hard to comprehend, for occurrence on the grounds that data about one class is normally
conveyed all through the tree. C4.5 presented an option formalism comprising of a rundown of standards of the structure
"if An and B and C and ... at that point class X", where rules for every class are gathered together. A case is arranged by
discovering the first control whose conditions are fulfilled by the case; if no standard is fulfilled, the case is relegated to a
default class. C4.5 rule sets are shaped from the starting (unpruned) choice tree. Every way from the root of the tree to a
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leaf turns into a model lead whose conditions are the results along the way and whose class is the name of the leaf. This
tenet is then rearranged by deciding the impact of disposing of every condition thusly. Dropping a condition may expand
the number N of cases secured by the standard; furthermore the number E of cases that don't fit in with the class selected
by the tenet, and may bring down the skeptical slip rate decided as above. A slope climbing calculation is utilized to drop
conditions until the least negative slip rate is found. To complete the procedure, a subset of disentangled tenets is chosen
for every class thusly. These class subsets are requested to minimize the blunder on the preparation cases and a default
class is picked. The last rule set normally has far less principles than the quantity of leaves on the pruned choice tree. The
central disservice of C4.5's rule sets is the measure of CPU time and memory that they require. In one analysis, tests going
from 6,000 to 60,000 cases were drawn from a substantial dataset. For choice trees, moving from 6 to 60K cases
expanded CPU time on a PC from 1.4 to 61 s, a component of 44. The time needed for rule sets, then again, expanded
from 32 to 9,715 s, an element of 300.
2.4 See5/C5.0
C4.5 was superseded in 1997 by a business framework See5/C5.0 (or C5.0 for short). The changes incorporate new
capacities and also highly enhanced effectiveness, and include:
• A variation of boosting [24], which develops a troupe of classifiers that are then voted to give a last characterization.
Boosting regularly prompts a sensational change in prescient exactness.
• New information sorts (e.g., dates), "not material" qualities, variable misclassification costs, and systems to prefilter
properties.
• Unordered rule sets—when a case is ordered, every pertinent tenet are discovered and voted. This enhances both the
interpretability of rule sets and their prescient precision.
• Greatly enhanced adaptability of both choice trees and (especially) rule sets. Versatility is improved by multi-threading;
C5.0 can exploit PCs with different CPUs and/or centers. More points of interest are accessible from http://rulequest.
com/see5-comparison.html.
2.5 Research issues
We have habitually heard associates express the perspective that choice trees are a "tackled issue." We don't concur with
this recommendation and will close with several open exploration issues.
Stable trees: It is surely understood that the slip rate of a tree on the cases from which it was built (the resubstitution
mistake rate) is much lower than the blunder rate on inconspicuous cases (the prescient mistake rate). Case in point, on a
surely understood letter acknowledgment dataset with 20,000 cases, the resubstitution mistake rate for C4.5 is 4%, yet the
slip rate from an abandon one-out (20,000-fold) cross-acceptance is 11.7%. As this illustrates, forgetting a solitary case
from 20,000 frequently influences the tree that is built! Assume now that we could build up a non-unimportant tree-
development calculation that was barely ever influenced by excluding a solitary case. For such stable trees, the
resubstitution slip rate ought to rough the abandon one-out cross-approved mistake rate, recommending that the tree is of
the "right" size.
Disintegrating complex trees: Troupe classifiers, whether produced by boosting, packing, weight randomization, or
different systems, generally offer enhanced prescient exactness. Presently, given a little number of choice trees, it is
conceivable to create a solitary (exceptionally complex) tree that is precisely equal to voting the first trees, yet would we
be able to go the other way? That is, can an unpredictable tree be separated to a little gathering of basic trees that, when
voted together, give the same result as the perplexing tree? Such disintegration would be of extraordinary help in creating
decision trees.
3. THE K-IMPLIES CALCULATION
3.1 The calculation
The k-implies calculation is a basic iterative strategy to segment a given dataset into a user specified number of bunches,
k. This calculation has been found by a few specialists crosswise over diverse controls, most eminently Lloyd (1957,
1982) [53], Forgey (1965), Friedman also, Rubin (1967), and McQueen (1967). An itemized history of k-means along
with depictions of a few varieties is given in [43]. Dim and Neuhoff [34] give a pleasant verifiable foundation for k-means
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put in the bigger connection of slope climbing calculations. The calculation works on an arrangement of d-dimensional
vectors, D = {xi | i = 1,,,,,, N}, where xi ∈ℜd indicates the ith information point. The calculation is introduced by picking
k focuses in ℜd as the beginning k group delegates or "centroids". Procedures for selecting these starting seeds
incorporate examining indiscriminately from the dataset, setting them as the arrangement of bunching a little subset of the
information or irritating the worldwide mean of the information k times. At that point the calculation repeats between two
stages till meeting:
Step 1: Data Assignment. Every information point is doled out to its nearest centroid, with ties broken discretionarily.
These outcomes in an apportioning of the information
Step 2: Relocation of "means". Every bunch agent is migrated to the inside (mean) of all information focuses allocated to
it. In the event that the information focuses accompany a likelihood measure (weights), then the movement is to the
desires (weighted mean) of the information allotments. The calculation converges when the assignments (and
consequently the cj values) no more change. The calculation execution is outwardly portrayed in Fig. 1. Note that every
emphasis needs N × k examinations, which decides the time unpredictability of one emphasis. The quantity of emphasess
needed for merging differs and may rely on upon N, yet as a first cut, this calculation can be viewed as direct in the
dataset size. One issue to determine is the way to measure "nearest" in the task step. The default measure of closeness is
the Euclidean separation, in which case one can promptly demonstrate that the non-negative expense capacity,
∑ ( )
Will diminish at whatever point there is an adjustment in the task or the migration steps, and consequently union is
ensured in a limited number of cycles. The avaricious drop nature of k-implies on a non-curved cost likewise infers that
the union is just to a nearby ideal, also, without a doubt the calculation is ordinarily very touchy to the introductory
centroid areas.
Figure outlines how a poorer result is gotten for the same dataset as in Fig. 1 for an alternate decision of the three beginning
centroids. The nearby minima issue cans degree by running the calculation various times with distinctive starting centroids, or
by doing restricted nearby inquiry about the merged arrangement.
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3.2 Limitations
Notwithstanding being touchy to introduction, the k-implies calculation experiences a few different issues. Initially, watch
that k-means is a restricting instance of fitting information by a blend of k Gaussians with indistinguishable, isotropic
covariance grids ( = σ2I), when the delicate assignments of information focuses to blend parts are solidified to apportion
every information point exclusively to the doubtlessly part. Along these lines, it will vacillate at whatever point the
information is not all around portrayed by sensibly isolated circular balls, for instance, if there are non-convex formed
bunches in the information. This issue may be allayed by rescaling the information to "brighten" it before bunching, then
again by utilizing an alternate separation measure that is more suitable for the dataset. For instance, data theoretic
bunching uses the KL-uniqueness to quantify the separation between two information focuses speaking to two discrete
likelihood circulations. It has been as of late demonstrated that in the event that one measures remove by selecting any
individual from a huge class of divergences called Bregman divergences amid the task step and rolls out no different
improvements, the fundamental properties of k-means, including ensured merging, direct division limits and versatility,
are held. This outcome makes k-implies powerful for a much bigger class of datasets insofar as a proper disparity is
utilized.
3.3 Generalizations and associations
As said before, k-means is firmly identified with fitting a blend of k isotropicGaussians to the information. Additionally,
the speculation of the separation measure to all Bergman divergences is identified with fitting the information with a
blend of k parts from the exponential group of appropriations. Another wide speculation is to view the "signifies" as
probabilistic models rather than focuses in Rd . Here, in the task step, every information point is allocated to the most
likely model to have created it. In the "migration" step, the model parameters are redesigned to best fit the allotted
datasets. Such model-based k-means permit one to indulge more complex information, e.g. successions depicted by
Hidden Markov models. One can likewise "kernelize" k-implies [19]. Despite the fact that limits between bunches are still
direct in the verifiable high-dimensional space, they can turn out to be non-straight when anticipated back to the first
space, therefore permitting part k-intends to manage more perplexing groups. Dhillon et al. [19] have demonstrated a
nearby association between part k-implies and ghastly bunching. The K-medoid calculation is like k-means aside from
that the centroids need to have a place with the information set being grouped. Fluffy c-means is likewise comparable,
with the exception of that it figures fluffy participation capacities for every groups as opposed to a hard one. In spite of its
downsides, k-means remains the most broadly utilized partitioned bunching calculation practically speaking. The
calculation is straightforward, effortlessly justifiable and sensibly adaptable, also, can be effortlessly altered to manage
spilling information. To manage huge datasets, significant exertion has additionally gone into further accelerating k-
implies, most outstandingly by utilizing kd-trees or misusing the triangular imbalance to abstain from contrasting every
information point and what not the centroids amid the task step.
4. SUPPORT VECTOR MACHINES
In today's machine learning applications, bolster vector machines (SVM) [83] are considered amust attempt it offers one
of the most strong and exact systems among all no doubt understood calculations. It has a sound hypothetical
establishment, requires just twelve illustrations for preparing, also, is heartless to the quantity of measurements. Also,
effective strategies for preparing SVM are additionally being produced at a quick pace. In a two-class learning
undertaking, the point of SVM is to locate the best grouping capacity to recognize individuals from the two classes in the
preparation information. The metric for the idea of the "best" grouping capacity can be acknowledged geometrically. For
a directly distinguishable dataset, a straight arrangement capacity relates to an isolating hyper plane f (x) that goes through
the center of the two classes, isolating the two. When this capacity is decided, new information occurrence xn can be
arranged by essentially testing the indication of the capacity f (xn); xn fits in with the positive class if f (xn) > 0. Since
there are numerous such straight hyper planes, what SVM furthermore ensure is that the best such capacity is found by
augmenting the edge between the two classes. Naturally, the edge is characterized as the measure of space, or detachment
between the two classes as characterized by the hyper plane. Geometrically, the edge relates to the briefest separation
between the nearest information focuses to a point on the hyper plane. Having this geometric definition permits us to
investigate how to boost the edge, so that despite the fact that there are a limitless number of hyper planes, just a couple
qualify as the answer for SVM. The motivation behind why SVM demands discovering the most extreme edge hyper
planes is that it offers the best speculation capacity. It permits not just the best arrangement execution (e.g., precision) on
the preparation information, additionally leaves much space for the right order of the future information.
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There are a few vital inquiries and related augmentations on the above fundamental definition of bolster vector machines.
We list these inquiries and augmentations beneath.
1. Could we comprehend the importance of the SVM through a strong hypothetical establishment?
2. Would we be able to extend the SVM plan to handle situations where we permit lapses to exist, at the point when even
the best hyper plane must concede a few blunders on the preparation information?
3. Would we be able to broaden the SVM definition so it lives up to expectations in circumstances where the preparation
information are not directly distinct?
4. Would we be able to broaden the SVM definition so that the assignment is to anticipate numerical qualities or to rank
the occasions in the probability of being a positive class part, instead of arrangement?
Question 1 Can we comprehend the importance of the SVM through a strong hypothetical establishment?
A few essential hypothetical results exist to answer this inquiry. A learning machine, for example, the SVM, can be
displayed as a capacity class in view of some parameters α. Diverse capacity classes can have distinctive limit in
realizing, which is spoken to by a parameter h known as the VC measurement [83]. The VC measurement measures the
greatest number of preparing samples where the capacity class can in any case be utilized to learn flawlessly, by acquiring
zero slip rates on the preparation information, for any task of class marks on these focuses. It can be demonstrated that the
genuine lapse on the future information is limited by an aggregate of two terms. The primary term is the preparation slip,
and the second term if relative to the square base of the VC measurement h. Along these lines, on the off chance that we
can minimize h, we can minimize what's to come blunder, the length of we likewise minimize the preparation mistake.
Actually, the above greatest edge capacity adapted by SVM learning calculations is one such capacity. Therefore,
hypothetically, the SVM calculation is very much established.
Question 2 Can we extend the SVM detailing to handle situations where we permit lapses to exist, when even the best
hyper plane must concede a few mistakes on the preparation information?
To answer this inquiry, envision that there are a couple purposes of the inverse classes that cross the center. These
focuses speak to the preparation mistake that current notwithstanding for the maximum edge hyper planes. The "delicate
edge" thought is gone for amplifying the SVM calculation [83] so that the hyper plane permits a couple of such boisterous
information to exist. Specifically, present a slack variable ξi to record for the measure of an infringement of grouping by
the capacity f (xi ); ξi has a direct geometric clarification through the separation from an erroneously characterized
information example to the hyper plane f (x). At that point, the aggregate expense presented by the slack variables can be
utilized to overhaul the first target minimization capacity.
5. THE APRIORI CALCULATION
5.1 Description of the calculation
A standout amongst the most prevalent information mining methodologies is to discover successive item sets from an
exchange dataset and infer affiliation rules. Discovering successive (item sets with recurrence bigger than or equivalent to
a client determined least backing) is not paltry on account of its combinatorial blast. Once visit item sets are gotten, it is
clear to produce affiliation rules with certainty bigger than or equivalent to a client indicated least certainty. Apriori is an
original calculation for discovering regular item sets utilizing applicant era. It is portrayed as a level-wise complete
inquiry calculation utilizing against monotonicity of item sets, "if an item set is not visit, any of its superset is never visit".
By tradition, Apriori expect that things inside of an exchange or item set are sorted in lexicographic request. Let the
arrangement of successive item sets of size k be Fk and their applicants be Ck . Apriori first sweeps the database and hunt
down incessant item sets of size 1 by aggregating the mean each thing and gathering those that fulfill the base bolster
prerequisite. It then emphasizes on the accompanying three stages and concentrates all the continuous item.
1. Create Ck+1, competitors of incessant item sets of size k +1, from the continuous item sets of size k.
2. Check the database and compute the backing of every applicant of continuous item sets.
3. Include that items set that fulfills the base bolster necessity to Fk+1. The Apriori calculation is indicated in Fig. 3.
Capacity apriori-gen in line 3 creates Ck+1 from Fk in the accompanying two stage process:
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1. Join step: Generate RK+1, the starting hopefuls of successive item sets of size k + 1 by
Taking the union of the two incessant item sets of size k, Pk and Qk that have the first k−1 components in like manner
RK+1 = Pk ∪ Qk = {i teml, . . . , i temk−1, i temk , i temk }
Pk = {i teml , i tem2, . . . , i temk−1, i temk }
Qk = {i teml , i tem2, . . . , i temk−1, i temk }
where, i teml < i tem2 < · < i temk < i temk.
2. Prune step: Check if all the item sets of size k in Rk+1 are visit and produce Ck+1 by evacuating those that don't pass
this necessity from Rk+1. This is on account of any subset of size k of Ck+1 that is not visit can't be a subset of a
successive item set of size k + 1. Capacity subset in line 5 discovers all the competitors of the incessant item sets included
in exchange t. Apriori, then, figures recurrence just for those applicants produced thusly by filtering the database. It is
apparent that Apriori filters the database at most kmax+1 times when the greatest size of continuous item sets is situated
at kmax. The Apriori accomplishes great execution by diminishing the extent of applicant set.
5.2 The effect of the calculation
A significant number of the example discovering calculations, for example, choice tree, grouping guidelines and bunching
procedures that are oftentimes utilized as a part of information mining have been produced in machine learning research
group. Continuous example and affiliation principle mining is one of only a handful couple of special cases to this
convention. The presentation of this method helped information mining examination and its effect is enormous. The
calculation is truly straightforward and simple to actualize. Testing with Apriori-like calculation is the first thing that
information mineworkers attempt to do.
5.3 Current and further research
Since Apriori calculation was initially presented and as experience was collected, there have been numerous endeavors to
devise more proficient calculations of regular item set mining. Numerous of them have the same thought with Apriori in
that they produce applicants. These incorporate hash-based system, dividing, testing and utilizing vertical information
design. Hash-based system can diminish the measure of applicant item sets. Each item set is hashed into a relating pail by
utilizing a fitting hash capacity. Since a can contain distinctive item sets, in the event that its number is not as much as a
base backing, these item sets in the can be expelled from the hopeful sets. An apportioning can be utilized to partition the
whole mining issue into n littler issues. The dataset is partitioned into n non-covering segments such that every allotment
fits into primary memory and every segment is mined independently. Since any item set that is conceivably visit as for the
whole dataset must happen as a continuous item set in no less than one of the parts, all the continuous item sets discovered
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this way are competitors, which can be checked by getting to the whole dataset just once. Inspecting is basically to mine
an irregular tested little subset of the whole information. Since there is no ensure that we can discover all the successive
item sets, typical practice is to utilize a lower bolster limit. Exchange off needs to be made in the middle of precision and
effectiveness. Apriori utilizes an even information form, i.e. incessant item sets are connected with every exchange.
Utilizing vertical information configuration is to utilize an alternate organization in which exchange IDs (TIDs) are
connected with each item set. With this configuration, mining can be performed by taking the crossing point of TIDs. The
bolster check is basically the length of the TID set for the item set. There is no compelling reason to output the database in
light of the fact that TID set conveys the complete data needed for registering backing. The most extraordinary change
over Apriori would be a strategy called FP-development (incessant example development) that succeeded in taking out
applicant era [36]. It receives a separation and vanquishes system by
(1) Packing the database speaking to successive things into a structure called FP-tree (regular example tree) that holds all
the key data what's more,
(2) Partitioning the packed database into an arrangement of contingent databases, each related with one continuous item
set and mining every one independently. It examines the database just twice.
In the first sweep, all the incessant things and their bolster tallies (frequencies) are inferred and they are sorted in the
request of slipping bolster tally in every exchange. In the second check, things in every exchange are converged into a
prefix tree and things (hubs) that show up in like manner in distinctive exchanges are checked. Every hub is connected
with a thing and its number. Hubs with the same name are connected by a pointer called hub join. Since things are sorted
in the dropping request of recurrence, hubs closer to the base of the prefix tree are shared by more exchanges, in this way
bringing about an extremely minimal representation that stores all the important data. Design development calculation
deals with FP-tree.
6. THE EM CALCULATION
Limited blend conveyances give an adaptable and numerical based way to deal with the displaying what's more, grouping
of information saw on irregular phenomena. We concentrate here on the utilization of ordinary blend models, which can
be utilized to group consistent information and to gauge the hidden thickness capacity. These blend models can be fitted
by most extreme probability by means of the EM (Expectation–Maximization) calculation.
6.1 Introduction
Limited mixture models are by and large progressively utilized to model the circulations of a wide mixed bag of irregular
phenomena and to group information sets. Here we consider their application in the setting of group investigation. We let
the p-dimensional vector ( y = (y1, . . . , yp)T) contain the estimations of p variables measured on each of n (autonomous)
substances to be bunched, and we let y j indicate the quality of y comparing to the j the substance ( j = 1, . . . , n).With the
blend way to deal with grouping, y1, . . . , yn are thought to be a watched arbitrary specimen from blend of a limited
number, say g, of gatherings in some obscure extents π1, . . . , πg. The blend thickness of y j is communicate
where the blending extents π1, . . . , πg whole to one and the gathering restrictive thickness fi (y j ; θi ) is determined up to
a vector θi of obscure parameters (i = 1, . . . , g). The vector of all the obscure parameters is give,
Where the subscript ―T‖ shows the vector transpose. Utilizing an evaluation of this methodology gives a probabilistic
grouping of the information into g groups as far as appraisals of the back probabilities of part participation,
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6.2 Maximum probability estimation of typical blends
McLachlan and Peel [57, Chap. 3] portrayed the E- and M-ventures of the EM algorithm for the most extreme probability
(ML) estimation of multivariate ordinary segments; see likewise. In the EM structure for this issue, the undetectable
segment marks zi j are dealt with as being the "missing" information, where zi j is characterized to be one or zero to the
extent that y j has a place or does not have a place with the ith segment of the blend (i = 1, . . . , g; , j = 1, . . . , n).
7. PAGE RANK
7.1 Overview
Page Rank was displayed and distributed by Sergey Brim and Larry Page at the Seventh Global World Wide Web
Conference (WWW7) in April 1998. It is a pursuit positioning calculation utilizing hyperlinks on the Web. Taking into
account the calculation, they manufactured the web search tool Google, which has been a tremendous achievement.
Presently, every web search tool has its own hyperlink based positioning technique. Page Rank produces a static
positioning of Web pages as in a Page Rank worth is figured for every page disconnected from the net and it doesn't rely
on upon inquiry questions. The calculation depends on the just way of the Web by utilizing its immense connection
structure as a marker of an individual page's quality. Fundamentally, Page Rank translates a hyperlink from page x to
page y as a vote, by page x, for page y. Be that as it may, Page Rank takes a gander at more than simply the sheer 123 18
X. Wu et al. number of votes, or connections that a page gets. It additionally breaks down the page that makes the choice.
Votes threw by pages that are themselves "essential" weigh all the more intensely and help to make different pages more
"essential". This is precisely the thought of rank esteem in informal communities.
7.2 The calculation
We now present the Page Rank equation. Give us a chance to first express some fundamental ideas in the Web setting. In-
connections of page i : These are the hyperlinks that indicate page i from different pages. Ordinarily, hyperlinks from the
same site are not considered. Out-connections of page i : These are the hyperlinks that call attention to different pages
from page i . Ordinarily, connections to pages of the same site are not considered. The accompanying thoughts taking into
account rank notoriety are utilized to infer the Page Rank calculation:
1. A hyperlink from a page indicating another page is a certain transport of power to the objective page. Subsequently, the
all the more in-connections that a page i gets, the more notoriety the page i has.
2. Pages that indicate page i additionally have their own particular eminence scores. A page with a higher renown score
indicating i is more imperative than a page with a lower esteem score indicating i . As it were, a page is imperative in the
event that it is indicated by other critical pages. As per rank eminence in interpersonal organizations, the significance of
page (i 's Page Rank score) is dictated by summing up the Page Rank scores of all pages that indicate i. Since a page may
indicate numerous different pages, its renowned score ought to be shared among all the pages that it indicates. To figure
the above thoughts, we regard the Web as a coordinated chart G = (V, E), where V is the situated of vertices or hubs, i.e.,
the arrangement of all pages, and E is the situated of coordinated edges in the chart, i.e., hyperlinks. Let the aggregate
number of pages on the Web be n (i.e., n = |V|).
The Page Rank score of the page i (indicated by P(i )) is characterized by,
8. CONCLUDING COMMENTS
Information mining is an expansive zone that incorporates methods from a few fields including machine learning,
measurements, design acknowledgment, manmade brainpower, and database frameworks, for the investigation of
substantial volumes of information. There have been an extensive number of information mining calculations attached in
these fields to perform diverse information examination assignments. The 6 calculations recognized by the IEEE
International Conference on Data Mining (ICDM) and introduced in 123 34 X. Wu et al. this article are among the most
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powerful calculations for order [47,51,77], grouping [11,31,40,44–46], measurable learning [28,76,92], affiliation
examination [2,6,13,50,54,74], also, connection mining. With a formal tie with the ICDM meeting, Knowledge and
Information Systems has been distributed the best papers from ICDM consistently, and a few of the above papers referred
to for order, grouping, measurable learning, and affiliation investigation were chosen by the earlier years' ICDM program
boards for diary distribution in Knowledge and Data Systems after their modifications and extensions. We trust this
review paper can motivate more analysts in information mining to further investigate these main 6 calculations, including
their effect and new research issues.
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