As the world is growing rapidly the people and the vehicles we use to move from one place to another, so the transportation is playing a vital role in making human lives easiest to travel from one place to another, everyday more and more vehicles are being produced and being bought by the people around the world, be it Electric, Hydrogen, petrol, diesel or solar powered.
Cancer data partitioning with data structure and difficulty independent clust...IRJET Journal
This document discusses cancer data partitioning using clustering techniques. It begins with an introduction to clustering concepts and different clustering methods like k-means, hierarchical agglomerative clustering, and partitioning methods. It then reviews literature on clustering algorithms and ensemble methods applied to problems like speaker diarization and tumor clustering from gene expression data. The document analyzes issues with existing clustering methodology and proposes a new dynamic ensemble membership selection scheme to support data structure and complexity independent clustering for cancer data partitioning. The method combines partition around medoids clustering with an incremental semi-supervised cluster ensemble framework to improve healthcare data partitioning accuracy.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document discusses multidimensional clustering methods for data mining and their industrial applications. It begins with an introduction to clustering, including definitions and goals. Popular clustering algorithms are described, such as K-means, fuzzy C-means, hierarchical clustering, and mixture of Gaussians. Distance measures and their importance in clustering are covered. The K-means and fuzzy C-means algorithms are explained in detail. Examples are provided to illustrate fuzzy C-means clustering. Finally, applications of clustering algorithms in fields such as marketing, biology, and earth sciences are mentioned.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
This document summarizes a paper that presents a framework called BRA that provides a bidirectional abstraction of asymmetric mobile ad hoc networks to enable off-the-shelf routing protocols to work. BRA maintains multi-hop reverse routes for unidirectional links, improves connectivity by using unidirectional links, enables reverse route forwarding of control packets, and detects packet loss on unidirectional links. Simulations show packet delivery increases substantially when AODV is layered on BRA in asymmetric networks compared to regular AODV.
Cancer data partitioning with data structure and difficulty independent clust...IRJET Journal
This document discusses cancer data partitioning using clustering techniques. It begins with an introduction to clustering concepts and different clustering methods like k-means, hierarchical agglomerative clustering, and partitioning methods. It then reviews literature on clustering algorithms and ensemble methods applied to problems like speaker diarization and tumor clustering from gene expression data. The document analyzes issues with existing clustering methodology and proposes a new dynamic ensemble membership selection scheme to support data structure and complexity independent clustering for cancer data partitioning. The method combines partition around medoids clustering with an incremental semi-supervised cluster ensemble framework to improve healthcare data partitioning accuracy.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document discusses multidimensional clustering methods for data mining and their industrial applications. It begins with an introduction to clustering, including definitions and goals. Popular clustering algorithms are described, such as K-means, fuzzy C-means, hierarchical clustering, and mixture of Gaussians. Distance measures and their importance in clustering are covered. The K-means and fuzzy C-means algorithms are explained in detail. Examples are provided to illustrate fuzzy C-means clustering. Finally, applications of clustering algorithms in fields such as marketing, biology, and earth sciences are mentioned.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
Bra a bidirectional routing abstraction for asymmetric mobile ad hoc networks...Mumbai Academisc
This document summarizes a paper that presents a framework called BRA that provides a bidirectional abstraction of asymmetric mobile ad hoc networks to enable off-the-shelf routing protocols to work. BRA maintains multi-hop reverse routes for unidirectional links, improves connectivity by using unidirectional links, enables reverse route forwarding of control packets, and detects packet loss on unidirectional links. Simulations show packet delivery increases substantially when AODV is layered on BRA in asymmetric networks compared to regular AODV.
This document describes a context-aware automatic traffic notification system for cell phones that can learn a user's common destinations and routes over time using location and context data. It collects GPS and other data from users, identifies important locations through clustering, learns frequent routes between locations, and can predict a user's destination and route to then notify them of any traffic conditions. The system is implemented on a mobile phone to provide automated traffic alerts to users during their daily commutes without needing to manually enter a destination.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
The document provides an overview of different clustering methods including partitioning methods like k-means and k-medoids, hierarchical methods like agglomerative and divisive, and density-based methods like DBSCAN and OPTICS. It discusses the basic concepts of clustering, requirements for effective clustering like scalability and ability to handle different data types and shapes. It also summarizes clustering algorithms like BIRCH that aim to improve scalability for large datasets.
Data analytics for engineers- introductionRINUSATHYAN
This document discusses key concepts in data analytics and statistics. It defines data and how data can be collected and used for decision making. It then discusses the evolution of analytic scalability, including traditional analytic architectures that pull all data into a separate environment for analysis, and modern in-database architectures that keep processing and analysis within the database. The document also covers statistical concepts like sampling, sampling frames, sampling designs, statistics versus parameters, sampling error, and definitions of mean, median, mode, and standard deviation.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
This document discusses various statistical analysis and feature engineering techniques that can be used for model building in machine learning algorithms. It describes how proper feature extraction through techniques like correlation analysis, principal component analysis, recursive feature elimination, and feature importance can help improve the accuracy of machine learning models. The document provides examples of applying different feature selection methods like univariate selection, recursive feature elimination, and principal component analysis on a diabetes dataset. It also explains the mathematics behind principal component analysis and how feature importance is estimated using an extra trees classifier. Overall, the document emphasizes how statistical analysis and feature engineering are important for effective model building in machine learning.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
UNIT 3: Data Warehousing and Data MiningNandakumar P
UNIT-III Classification and Prediction: Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section.
The document discusses clustering and its applications in contour detection. It notes that while clustering is widely used to organize unlabeled data and remove noise, there are still challenges. Specifically, selecting an appropriate data set, determining the number of clusters, and validating results can be ambiguous. Clustering algorithms are also sensitive to these parameters and the data set properties. Contour extraction methods also lack efficiency and universality. Improved clustering techniques are needed that can be more effectively applied to contour detection problems across different data sets.
Data preprocessing is required because real-world data is often incomplete, noisy, inconsistent, and in an aggregate form. The goals of data preprocessing include handling missing data, smoothing out noisy data, resolving inconsistencies, computing aggregate attributes, reducing data volume to improve mining performance, and improving overall data quality. Key techniques for data preprocessing include data cleaning, data integration, data transformation, and data reduction.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
The document describes a system that uses data mining techniques and GPS to guide ships based on weather conditions. GPS is used to determine a ship's location, which is then compared to a weather report database using classification models. A decision tree is generated from the training data to predict weather conditions and determine if it is safe for the ship to continue its course. When the ship's location is received via GPS, the decision tree is used to analyze weather data for that area and send guidance to the ship about navigating safely.
The document examines using a nearest neighbor algorithm to rate men's suits based on color combinations. It trained the algorithm on 135 outfits rated as good, mediocre, or bad. It then tested the algorithm on 30 outfits rated by a human. When trained on 135 outfits, the algorithm incorrectly rated 36.7% of test outfits. When trained on only 68 outfits, it incorrectly rated 50% of test outfits, showing larger training data improves accuracy. It also tested using HSL color representation instead of RGB with similar results.
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.
Histogram-Based Method for Effective Initialization of the K-Means Clustering...Gingles Caroline
This document proposes a histogram-based method for initializing cluster centers in k-means clustering. The method works by recursively finding the most populated histogram bin for each attribute dimension, using the bin centroid as the coordinate for that dimension. This focuses the cluster centers on dense regions of the data distribution. The method is linear in complexity, deterministic, and order-invariant, making it suitable for large datasets where other initialization methods are impractical or unreliable. Experimental results on UCI datasets show it outperforms the commonly used maximin initialization method.
The document discusses machine learning algorithms including logistic regression, random forests, support vector machines (SVM), and analysis of variance (ANOVA). It provides descriptions of how each algorithm works, its advantages, and examples of applications. Logistic regression uses a sigmoid function to predict binary outcomes. Random forests create an ensemble of decision trees to make classifications. SVM finds the optimal separating hyperplane between classes. ANOVA splits variability in a data set into systematic and random factors.
The document discusses using k-means clustering on a life insurance customer dataset to predict customer preferences. It first provides background on k-means clustering and its application in data mining. It then describes applying k-means to a dataset of 14,180 customer records with 10 attributes from an Albanian insurance company. This identified 5 clusters characterizing different customer segments based on attributes like gender, age, and preferred insurance product type and amount. The results help the insurance company better understand customer preferences to improve performance.
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
Exciting IoT projects for your final year.pdfjagan477830
The final year presents an opportunity for students to engage in exciting Internet of Things (IoT) projects. These projects offer a platform for students to apply their knowledge and skills in developing innovative solutions that address real-world challenges. The IoT projects provide a valuable learning experience that prepares students for the demands of the modern workplace.
Innovative IoT-Based Projects to Revolutionize Everyday Life.pdfjagan477830
Welcome to the presentation on Transforming Daily Living: Unleashing the Potential of Innovative IoT-Based Projects. Today, we will explore the exciting advancements and possibilities that arise from integrating Internet of Things (IoT) technologies into our everyday live
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This document describes a context-aware automatic traffic notification system for cell phones that can learn a user's common destinations and routes over time using location and context data. It collects GPS and other data from users, identifies important locations through clustering, learns frequent routes between locations, and can predict a user's destination and route to then notify them of any traffic conditions. The system is implemented on a mobile phone to provide automated traffic alerts to users during their daily commutes without needing to manually enter a destination.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
The document provides an overview of different clustering methods including partitioning methods like k-means and k-medoids, hierarchical methods like agglomerative and divisive, and density-based methods like DBSCAN and OPTICS. It discusses the basic concepts of clustering, requirements for effective clustering like scalability and ability to handle different data types and shapes. It also summarizes clustering algorithms like BIRCH that aim to improve scalability for large datasets.
Data analytics for engineers- introductionRINUSATHYAN
This document discusses key concepts in data analytics and statistics. It defines data and how data can be collected and used for decision making. It then discusses the evolution of analytic scalability, including traditional analytic architectures that pull all data into a separate environment for analysis, and modern in-database architectures that keep processing and analysis within the database. The document also covers statistical concepts like sampling, sampling frames, sampling designs, statistics versus parameters, sampling error, and definitions of mean, median, mode, and standard deviation.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
This document discusses various statistical analysis and feature engineering techniques that can be used for model building in machine learning algorithms. It describes how proper feature extraction through techniques like correlation analysis, principal component analysis, recursive feature elimination, and feature importance can help improve the accuracy of machine learning models. The document provides examples of applying different feature selection methods like univariate selection, recursive feature elimination, and principal component analysis on a diabetes dataset. It also explains the mathematics behind principal component analysis and how feature importance is estimated using an extra trees classifier. Overall, the document emphasizes how statistical analysis and feature engineering are important for effective model building in machine learning.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
UNIT 3: Data Warehousing and Data MiningNandakumar P
UNIT-III Classification and Prediction: Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section.
The document discusses clustering and its applications in contour detection. It notes that while clustering is widely used to organize unlabeled data and remove noise, there are still challenges. Specifically, selecting an appropriate data set, determining the number of clusters, and validating results can be ambiguous. Clustering algorithms are also sensitive to these parameters and the data set properties. Contour extraction methods also lack efficiency and universality. Improved clustering techniques are needed that can be more effectively applied to contour detection problems across different data sets.
Data preprocessing is required because real-world data is often incomplete, noisy, inconsistent, and in an aggregate form. The goals of data preprocessing include handling missing data, smoothing out noisy data, resolving inconsistencies, computing aggregate attributes, reducing data volume to improve mining performance, and improving overall data quality. Key techniques for data preprocessing include data cleaning, data integration, data transformation, and data reduction.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
The document describes a system that uses data mining techniques and GPS to guide ships based on weather conditions. GPS is used to determine a ship's location, which is then compared to a weather report database using classification models. A decision tree is generated from the training data to predict weather conditions and determine if it is safe for the ship to continue its course. When the ship's location is received via GPS, the decision tree is used to analyze weather data for that area and send guidance to the ship about navigating safely.
The document examines using a nearest neighbor algorithm to rate men's suits based on color combinations. It trained the algorithm on 135 outfits rated as good, mediocre, or bad. It then tested the algorithm on 30 outfits rated by a human. When trained on 135 outfits, the algorithm incorrectly rated 36.7% of test outfits. When trained on only 68 outfits, it incorrectly rated 50% of test outfits, showing larger training data improves accuracy. It also tested using HSL color representation instead of RGB with similar results.
Perfomance Comparison of Decsion Tree Algorithms to Findout the Reason for St...ijcnes
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Histogram-Based Method for Effective Initialization of the K-Means Clustering...Gingles Caroline
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
2. Introduction
► As the world is growing rapidly the people and the vehicles we use to move
from one place to another, so the transportation is playing a vital role in
making human lives easiest to travel from one place to another, everyday more
and more vehicles are being produced and being bought by the people around
the world, be it Electric, Hydrogen, petrol, diesel or solar powered. So, most
importantly Road Transportation such as, Road transport can be classified as
either transporting goods and materials or transporting people. The main
advantage of road transportation is that it allows for door-to-door delivery of
goods and materials while also being a very cost-effective mode of cartage,
loading, and unloading. Road transport is sometimes the only option for
transporting goods and people to and from rural areas that are not served by
rail, water, or air transport. Road transportation has numerous advantages over
other modes of transportation. Road transport requires significantly less
investment than other modes of transportation such as railways and air
transport. Roads are less expensive to build, operate, and maintain than
railways.
3. Dataset Description
► The dataset is collected from the Kaggle data repository,(US Accidents (2016
- 2021)
► Dataset is in Comma Separated Value format, It consists of 2845342 entries,
Ranging from 0 to 2845341, which has 47 columns.
► Since the dataset is very huge and contains many columns, we are
going to discuss about important columns over here.
1. Severity – Type(int), this columns explains about the severity of the accident, and importantly this is our
target class for making prediction further in the project.
2. Start_time & End_time – Type(object), This shows the start and end time of the accident took palce at
certain place, similarly, we have latitude and longitude coordinates of the accident place, since the dataset is
about accidents taken place in US.
3. Distance – Length of the road extent affected by the accident occurred.
4. Description – Explains about the description of the accidents given by the fellow drivers who were driving
along side with the accident victims.
5. City, State, County – Explains about the place where the accident took place, in which specific city, state
and county.
6. Along with these, we also have other columns such as weather, temperature, traffic signal, sunrise_sunset,
railway_line etc.
7. Descriptive Analysis
► Here we are going to dive deep into the dataset and know some more
information about it.
► Below functions helps us to understand the insights of the data and also helps
us to extract information which might help us to fill the null values.
1. df.info() -> information about the dataset, such as type of each column and
the numebr of entries present in the dataset.
2. df.describe() -> helps us to understand the descriptive data of each column,
note: the description for numerical and categorical will be different, by
default we get the numerical column description.
3. df.isnull().sum() -> Count of Missing Values for each column.
4. df.head() -> Displays first 5 rows of the dataset, similarly df.tail displays last
5.
13. Since temperature has less than 10% null values of the total number of values and they
appear to be normally distributed. It might be a good idea to fill these empty data with
the mean value. Whereas for Visibility(mi), it's right skewed. So replacing null values
with a median value is more suitable.
Since Precipitation(in), Wind_Speed(mph) have an right skewed distribution. It's better
to use mode value to fill the Null value in these two columns. Humidity(%) though has
a left skewed distribution. I still used the mode value to fill out the Null. It may not be
accurate to fill out the Null value based on the previous or latter adjacent value, as
every two accidents were hardly related.
Also, Most of the columns were Irrelevant and consisted of more than 60% of missing
values, so I decided to drop those features.
15. Predictive Analysis
► Predictive analytics uses mathematical modeling tools to generate predictions
about an unknown fact, characteristic, or event. “It’s about taking the data that
you know exists and building a mathematical model from that data to help you
make predictions about somebody not yet in that data set,” Goulding explains.
► An analyst’s role in predictive analysis is to assemble and organize the data,
identify which type of mathematical model applies to the case at hand, and
then draw the necessary conclusions from the results. They are often also
tasked with communicating those conclusions to stakeholders effectively and
engagingly.
► “The tools we’re using for predictive analytics now have improved and
become much more sophisticated,” Goulding says, explaining that these
advanced models have allowed us to “handle massive amounts of data in ways
we couldn’t before.”
► Example: Linear Regression, Logistic Regression, Decision Trees, Random
Forest, Support Vector Machines etc.
16. Cluster Analysis
► Clustering is the process of dividing a population or set of data points into
groups so that data points in the same group are more similar to other data
points in the same group and dissimilar to data points in other groups. It is
essentially a collection of objects based on their similarity and dissimilarity.
► Cluster analysis itself is not one specific algorithm but the general task to be
solved. It can be achieved by various algorithms that differ significantly in
their understanding of what constitutes a cluster and how to efficiently find
them. Popular notions of clusters include groups with small distances between
cluster members, dense areas of the data space, intervals or particular
Statistical distributions.
► Clustering can therefore be formulated as a multi-objective
optimization problem. The appropriate clustering algorithm and parameter
settings (including parameters such as the distance function to use, a density
threshold or the number of expected clusters) depend on the individual data
set and intended use of the results.
17.
18. Random Forest
► Random Forest is a supervised machine learning algorithm. This Technique can be
used for both regression and classification tasks but generally performs better in
classification tasks. As the name suggests, Random Forest technique considers
multiple decision trees before giving an output. So, it is basically an ensemble of
decision trees.
► This technique is based on the belief that a greater number of trees would converge
to the right decision. For classification, it uses a voting system and then decides the
class whereas in regression it takes the mean of all the outputs of each of the
decision trees.
► It works well with large datasets with high dimensionality. The random forest
algorithm is an extension of the bagging method as it utilizes both bagging and
feature randomness to create an uncorrelated forest of decision trees. Feature
randomness, also known as feature bagging or “the random subspace method
generates a random subset of features, which ensures low correlation among
decision trees.
20. KNearest Neighbors
► The k-nearest neighbor algorithm, also known as KNN or k-NN, is a
non-parametric, supervised learning classifier that uses proximity to classify or
predict the grouping of an individual data point. It can be used for both regression
and classification problems, but it is most commonly used as a classification
algorithm, based on the assumption that similar points can be found close together.
► A majority vote is used to assign a class label to a classification problem that is, the
label that is most frequently represented around a given data point is used. While
technically this is referred to as "plurality voting," the term "majority vote" is more
commonly used in literature.
► The difference between these terms is that "majority voting" technically requires a
majority of more than 50%, which only works when there are only two options.
When there are multiple classes say, four categories you don't always need 50% of
the vote to make a decision about a class; you could assign a class label with a vote
of more than 25%.
22. Variable Selection Method
► Feature or Variable selection methods are used to select specific features from our dataset, which are useful and important
for our model to learn and predict. As a result, feature selection is an important step in the development of a machine
learning model. Its goal is to identify the best set of features for developing a machine learning model.
► Some popular techniques of feature selection in machine learning are:
• Filter methods
• Wrapper methods
• Embedded methods
► Filter Methods
• These methods are generally used while doing the pre-processing step. These methods select features from the dataset
irrespective of the use of any machine learning algorithm.
• Techniques such as : Information gain, Chi-Square, Variance_Threshold, Mean_Absolute_Difference etc.
► Wrapper methods:
• Wrapper methods, also referred to as greedy algorithms train the algorithm by using a subset of features in an iterative
manner. Based on the conclusions made from training in prior to the model, addition and removal of features takes place.
• Techniques such as: Forward selection, Backward Elimination, Bi-Directional Elimination etc.
► Embedded methods:
• In embedded methods, the feature selection algorithm is blended as part of the learning algorithm, thus having its own
built-in feature selection methods. Embedded methods encounter the drawbacks of filter and wrapper methods and merge
their advantages.
• Techniques such as: Regularization, tree based methods
23. Variable selection using SequentialFeatureSelection
► Sequential feature selection algorithms are a type of greedy search algorithm that is
used to reduce a d-dimensional feature space to a k-dimensional feature subspace,
where k d. Feature selection algorithms are designed to automatically select a subset of
features that are most relevant to the problem.
► A wrapper approach, such as sequential feature selection, is especially useful when
embedded feature selection, such as a regularization penalty like LASSO, is not
applicable.
► SFAs, in a nutshell, remove or add features one at a time based on classifier
performance until a feature subset of the desired size k is reached.
► There are basically 4 types of SFA’s such as:
1. Sequential Forward Selection (SFS)
2. Sequential Backward Selection (SBS)
3. Sequential Forward Floating Selection (SFFS)
4. Sequential Backward Floating Selection (SBFS)
► The one we have employed in our project is the Sequential forward selection
25. Testing the Model on Variables selected by
algorithm.
Decision Tree
► A decision tree is a decision support tool that uses a tree-like model of decisions and their possible
consequences, including chance event outcomes, resource costs, and utility. It is one way to display
an algorithm that only contains conditional control statements. Decision trees are commonly used
in operations research, specifically in decision analysis, to help identify a strategy most likely to reach
a goal but are also a popular tool in machine learning. A decision tree is a flowchart-like structure in
which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or
tails), each branch represent the outcome of the test, and each leaf node represents a class label
(decision taken after computing all attributes). The paths from root to leaf represent classification
rules. In decision analysis, a decision tree and the closely related influence diagram are used as a
visual and analytical decision support tool, where the expected values (or expected utility) of
competing alternatives are calculated.
► A decision tree consists of three types of nodes
► Decision nodes – typically represented by squares
► Chance nodes – typically represented by circles
► End nodes – typically represented by triangles
26. Using Decision Tree as a classifier, we have fitted a sequential feature selector
model to extract the important features from the dataset.
27. Sfal. Subsets_ -> Explains about the average accuracy we got by training the model
for number of features in each step.
28.
29. Plot about the important features extracted from Sequential Feature
Selector, X axis represents numebr of features and Y axis represents
prediction accuracy we got by selecting those specific features.
30. Results are converted into a dataframe where 1st column represents the number
of features and 2nd
column represents the accuracy we got from selecting those
features.
31. Conclusion
► In this project, we have done a lot of preprocessing and exploratory data
analysis, since the main objective was to get insights from the road
transportation data and do statistical analysis.
► Data preprocessing has been performed by filling in the null vlaues and
dropping of irrelevant columns based on how important they are for building
an efficient model keeping computational cost in mind.
► Predictive models such as Decision tree, Random Forest and KNearest
Neighbors Classification algorithms has been applied to predict the target
variable i.e Severity of the accident using the other independent features.
► Variable selection methods such as Sequential Feature Selector has been
applied to the cleaned data to extract the most important features, and those
features are trained and tested on the Decision tree model.
32. About TechieYan Technologies
TechieYan Technologies offers a special platform where you can study all the most
cutting-edge technologies directly from industry professionals and get
certifications. TechieYan collaborates closely with engineering schools,
engineering students, academic institutions, the Indian Army, and businesses.
Project trainings, engineering workshops, internships, and laboratory setup are all
things we provide. We work on projects related to robotics, python, deep learning,
artificial intelligence, IoT, embedded systems, matlab, hfss pcb design, vlsi, and
ieee current projects.
Address: 16-11-16/V/24, Sri Ram Sadan, Moosarambagh, Hyderabad 500036
phone no: +91 7075575787
website:https://techieyantechnologies.com