Knowledge Discovery Tutorial By Claudia d'Amato and Laura Hollnik at the Summer School on Ontology Engineering and the Semantic Web in Bertinoro, Italy (SSSW2015)
This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified interface to a large number of R’s modeling functions and enables parallel processing. Participants should have a basic understanding of R data structures and basic language elements (i.e. functions, classes, etc).
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
This document provides an overview of machine learning classification and decision trees. It discusses key concepts like supervised vs. unsupervised learning, and how decision trees work by recursively partitioning data into nodes. Random forest and gradient boosted trees are introduced as ensemble methods that combine multiple decision trees. Random forest grows trees independently in parallel while gradient boosted trees grow sequentially by minimizing error from previous trees. While both benefit from ensembling, gradient boosted trees are more prone to overfitting and random forests are better at generalizing to new data.
This document provides a summary of Bayesian classification. Bayesian classification predicts the probability of class membership for new data instances based on prior knowledge and training data. It uses Bayes' theorem to calculate the posterior probability of a class given the attributes of an instance. The naive Bayesian classifier assumes attribute independence and uses frequency counts to estimate probabilities. It classifies new instances by selecting the class with the highest posterior probability. The example shows how probabilities are estimated from training data and used to classify an unseen instance in the play-tennis dataset.
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.
This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified interface to a large number of R’s modeling functions and enables parallel processing. Participants should have a basic understanding of R data structures and basic language elements (i.e. functions, classes, etc).
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
This document provides an overview of machine learning classification and decision trees. It discusses key concepts like supervised vs. unsupervised learning, and how decision trees work by recursively partitioning data into nodes. Random forest and gradient boosted trees are introduced as ensemble methods that combine multiple decision trees. Random forest grows trees independently in parallel while gradient boosted trees grow sequentially by minimizing error from previous trees. While both benefit from ensembling, gradient boosted trees are more prone to overfitting and random forests are better at generalizing to new data.
This document provides a summary of Bayesian classification. Bayesian classification predicts the probability of class membership for new data instances based on prior knowledge and training data. It uses Bayes' theorem to calculate the posterior probability of a class given the attributes of an instance. The naive Bayesian classifier assumes attribute independence and uses frequency counts to estimate probabilities. It classifies new instances by selecting the class with the highest posterior probability. The example shows how probabilities are estimated from training data and used to classify an unseen instance in the play-tennis dataset.
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.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
This document discusses classification using decision tree models. It begins with an introduction to classification, describing it as assigning objects to predefined categories. Decision trees are then overviewed as a powerful classifier that uses a hierarchical structure to split a dataset. Important parameters for evaluating model accuracy are covered, such as precision, recall, and AUC. The document also describes an exercise using the Weka tool to build decision trees on a dataset about term deposit subscriptions. It concludes with discussing uses of decision trees for applications like marketing and medical diagnosis.
This document discusses data and attributes in data mining. It defines data as a collection of objects and their properties or attributes. Attributes can be nominal, ordinal, interval or ratio. The document describes different types of attributes and data sets, as well as important characteristics like dimensionality and sparsity. It also covers data quality issues, preprocessing techniques like aggregation, sampling and feature selection, and measures of similarity and dissimilarity between data objects.
This document provides an overview of a SQL Server 2008 for Business Intelligence short course. It discusses the course instructor's background and specialties. The course will cover creating a data warehouse, OLAP cubes, and reports. It will also discuss data mining concepts like why it's used, common algorithms, and include a hands-on lab. Data mining algorithms that will be covered include classification, clustering, decision trees, and neural networks.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Unsupervised Learning Techniques to Diversifying and Pruning Random ForestMohamed Medhat Gaber
The document discusses techniques for diversifying and pruning random forests for data classification. It covers background topics on data classification, ensemble classification, and random forests. It then describes two proposed techniques: CLUB-DRF which uses clustering to build a diverse random forest, and LOFB-DRF which uses outlier scoring. The document concludes with a summary and discussion of future work.
An efficient data preprocessing method for miningKamesh Waran
This document proposes an efficient data preprocessing method for mining customer survey data using a unified data model. Traditional preprocessing requires transforming raw data separately for each data mining algorithm, requiring significant time. The proposed method defines a standard unified data model based on survey data characteristics. Raw data is mapped to this model, reducing the number of transformations from multiple per algorithm to just one per data set. This unified approach saves substantial time in preprocessing while maintaining flexibility for different mining tools.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document discusses classification and prediction techniques in data mining. It covers various classification methods like decision tree induction, Bayesian classification, and support vector machines. It also discusses scaling classification to large databases, evaluating model accuracy, and presenting classification results visually. The key methods covered are decision tree construction using information gain, the naïve Bayesian classifier based on Bayes' theorem, and scaling tree learning using techniques like RainForest.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
Handling Imbalanced Data: SMOTE vs. Random UndersamplingIRJET Journal
This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.
This document summarizes a study that used various machine learning techniques to classify customers as either 2G or 3G network users based on their usage data. It discusses preprocessing the data, building models using decision trees, lazy classifiers, and boosting, and combining the models' predictions. The best performing technique was boosting decision trees, which correctly classified 88.58% of customers through 10-fold cross-validation. While imperfect, combining multiple models led to more reliable classification than any single model.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
CART: Not only Classification and Regression TreesMarc Garcia
Decision trees are very simple methods compared to Support Vector Machines, or Deep Learning. But they have some interesting properties that make them unique. For classification, for regression, or to extract probabilities, decision trees are easy to set up, and debug. And they are excellent to get a better understanding of your data.
This talk will cover Decision Trees, from theory, to their implementation in Python.
The talk will have a very practical approach, using examples and real cases to illustrate how to use decision trees, what we can expect from using them, and what kind of problems we will need to address.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Data preprocessing for decision trees
* Understanding your data better with decision tree visualization
* Debugging decision trees using common sense and prior domain knowledge
* Avoiding overfitting, without cross-validation
* Python implementation
* Performance
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
This document provides an overview of data mining segmentation techniques. It discusses the value of data mining, common segmentation methodologies, tools in SQL Server Analysis Services for segmentation, and ways to build confidence in segmentation models. Key points include discussing unsupervised vs supervised learning for segmentation, preparing data sources and refining data, using algorithms like decision trees and clustering for segmentation analysis, and improving models through changing algorithms, parameters, or cleaning the data.
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
The document provides an overview of machine learning activities including data exploration, preprocessing, model selection, training and evaluation. It discusses exploring different data types like numerical, categorical, time series and text data. It also covers identifying and addressing data issues, feature engineering, selecting appropriate models for supervised and unsupervised problems, training models using methods like holdout and cross-validation, and evaluating model performance using metrics like accuracy, confusion matrix, F-measure etc. The goal is to understand the data and apply necessary steps to build and evaluate effective machine learning models.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
This document discusses classification using decision tree models. It begins with an introduction to classification, describing it as assigning objects to predefined categories. Decision trees are then overviewed as a powerful classifier that uses a hierarchical structure to split a dataset. Important parameters for evaluating model accuracy are covered, such as precision, recall, and AUC. The document also describes an exercise using the Weka tool to build decision trees on a dataset about term deposit subscriptions. It concludes with discussing uses of decision trees for applications like marketing and medical diagnosis.
This document discusses data and attributes in data mining. It defines data as a collection of objects and their properties or attributes. Attributes can be nominal, ordinal, interval or ratio. The document describes different types of attributes and data sets, as well as important characteristics like dimensionality and sparsity. It also covers data quality issues, preprocessing techniques like aggregation, sampling and feature selection, and measures of similarity and dissimilarity between data objects.
This document provides an overview of a SQL Server 2008 for Business Intelligence short course. It discusses the course instructor's background and specialties. The course will cover creating a data warehouse, OLAP cubes, and reports. It will also discuss data mining concepts like why it's used, common algorithms, and include a hands-on lab. Data mining algorithms that will be covered include classification, clustering, decision trees, and neural networks.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Unsupervised Learning Techniques to Diversifying and Pruning Random ForestMohamed Medhat Gaber
The document discusses techniques for diversifying and pruning random forests for data classification. It covers background topics on data classification, ensemble classification, and random forests. It then describes two proposed techniques: CLUB-DRF which uses clustering to build a diverse random forest, and LOFB-DRF which uses outlier scoring. The document concludes with a summary and discussion of future work.
An efficient data preprocessing method for miningKamesh Waran
This document proposes an efficient data preprocessing method for mining customer survey data using a unified data model. Traditional preprocessing requires transforming raw data separately for each data mining algorithm, requiring significant time. The proposed method defines a standard unified data model based on survey data characteristics. Raw data is mapped to this model, reducing the number of transformations from multiple per algorithm to just one per data set. This unified approach saves substantial time in preprocessing while maintaining flexibility for different mining tools.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document discusses classification and prediction techniques in data mining. It covers various classification methods like decision tree induction, Bayesian classification, and support vector machines. It also discusses scaling classification to large databases, evaluating model accuracy, and presenting classification results visually. The key methods covered are decision tree construction using information gain, the naïve Bayesian classifier based on Bayes' theorem, and scaling tree learning using techniques like RainForest.
Statistics For Data Science | Statistics Using R Programming Language | Hypot...Edureka!
( ** Data Science Certification Using R: https://www.edureka.co/data-science ** )
This Edureka tutorial on "Statistics for Data Science" talks about the basic concepts of Statistics, which is primarily an applied branch of mathematics, that attempts to make sense of observations in the real world. Statistics is generally regarded as one of the most crucial aspects of data science.
Introduction to statistics
Basic Terminology
Categories in Statistics
Descriptive Statistics
Reasons for moving to R
Descriptive Statistics in R Studio
Inferential Statistics
Inferential Statistics using R Studio
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
This document discusses various techniques for data preprocessing, including data cleaning, integration, transformation, and reduction. It describes why preprocessing is important for obtaining quality data and mining results. Key techniques covered include handling missing data, smoothing noisy data, data integration and normalization for transformation, and data reduction methods like binning, discretization, feature selection and dimensionality reduction.
Handling Imbalanced Data: SMOTE vs. Random UndersamplingIRJET Journal
This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.
This document summarizes a study that used various machine learning techniques to classify customers as either 2G or 3G network users based on their usage data. It discusses preprocessing the data, building models using decision trees, lazy classifiers, and boosting, and combining the models' predictions. The best performing technique was boosting decision trees, which correctly classified 88.58% of customers through 10-fold cross-validation. While imperfect, combining multiple models led to more reliable classification than any single model.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
CART: Not only Classification and Regression TreesMarc Garcia
Decision trees are very simple methods compared to Support Vector Machines, or Deep Learning. But they have some interesting properties that make them unique. For classification, for regression, or to extract probabilities, decision trees are easy to set up, and debug. And they are excellent to get a better understanding of your data.
This talk will cover Decision Trees, from theory, to their implementation in Python.
The talk will have a very practical approach, using examples and real cases to illustrate how to use decision trees, what we can expect from using them, and what kind of problems we will need to address.
The main topics covered will include:
* What are decision trees?
* How decision trees are trained?
* Data preprocessing for decision trees
* Understanding your data better with decision tree visualization
* Debugging decision trees using common sense and prior domain knowledge
* Avoiding overfitting, without cross-validation
* Python implementation
* Performance
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
This document provides an overview of data mining segmentation techniques. It discusses the value of data mining, common segmentation methodologies, tools in SQL Server Analysis Services for segmentation, and ways to build confidence in segmentation models. Key points include discussing unsupervised vs supervised learning for segmentation, preparing data sources and refining data, using algorithms like decision trees and clustering for segmentation analysis, and improving models through changing algorithms, parameters, or cleaning the data.
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
The document provides an overview of machine learning activities including data exploration, preprocessing, model selection, training and evaluation. It discusses exploring different data types like numerical, categorical, time series and text data. It also covers identifying and addressing data issues, feature engineering, selecting appropriate models for supervised and unsupervised problems, training models using methods like holdout and cross-validation, and evaluating model performance using metrics like accuracy, confusion matrix, F-measure etc. The goal is to understand the data and apply necessary steps to build and evaluate effective machine learning models.
The document discusses data warehousing, data mining, and business intelligence applications. It explains that data warehousing organizes and structures data for analysis, and that data mining involves preprocessing, characterization, comparison, classification, and forecasting of data to discover knowledge. The final stage is presenting discovered knowledge to end users through visualization and business intelligence applications.
The document discusses data mining and knowledge discovery in databases. It defines data mining as extracting patterns from large amounts of data. The key steps in the knowledge discovery process are presented as data selection, preprocessing, data mining, and interpretation. Common data mining techniques include clustering, classification, and association rule mining. Clustering groups similar data objects, classification predicts categorical labels, and association rules find relationships between variables. Data mining has applications in many domains like market analysis, fraud detection, and bioinformatics.
This document outlines a course on knowledge acquisition in decision making, including the course objectives of introducing data mining techniques and enhancing skills in applying tools like SAS Enterprise Miner and WEKA to solve problems. The course content is described, covering topics like the knowledge discovery process, predictive and descriptive modeling, and a project presentation. Evaluation includes assignments, case studies, and a final exam.
This document outlines the objectives, content, evaluation, and prerequisites for a course on Knowledge Acquisition in Decision Making, which introduces students to data mining techniques and how to apply them to solve business problems using SAS Enterprise Miner and WEKA. The course covers topics such as data preprocessing, predictive modeling with decision trees and neural networks, descriptive modeling with clustering and association rules, and a project presentation. Students will be evaluated based on assignments, case studies, a project, quizzes, class participation, and a final exam.
Introduction to the implementation of Data Science projects in organizations, with a practice session on how to apply machine-learning techniques to a business problem.
Notebook of the practice session is available at https://github.com/klinamen/ds0-experimenting-with-data
Data preprocessing transforms raw data into a format that is suitable for machine learning algorithms. It involves cleaning data by handling missing values, outliers, and inconsistencies. Dimensionality reduction techniques like principal component analysis are used to reduce the number of features by creating new features that are combinations of the originals. Feature encoding converts categorical features into numeric values that machines can understand through techniques like one-hot encoding. The goal of preprocessing is to prepare data so machine learning algorithms can more easily interpret features and patterns in the data.
This document discusses various data mining techniques, including artificial neural networks. It provides an overview of the knowledge discovery in databases process and the cross-industry standard process for data mining. It also describes techniques such as classification, clustering, regression, association rules, and neural networks. Specifically, it discusses how neural networks are inspired by biological neural networks and can be used to model complex relationships in data.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
Lesson 1 - Overview of Machine Learning and Data Analysis.pptxcloudserviceuit
This document provides an overview of machine learning and data analysis. It defines machine learning as a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. The main types of machine learning are supervised, unsupervised, and reinforcement learning. Data analysis is the process of extracting meaningful insights from data through techniques like cleaning, exploring for patterns/trends, statistical analysis, and visualization. Machine learning automates many data analysis tasks and can be applied through techniques like classification, clustering, and regression. The relationship between machine learning and data analysis fuels discovery, with data analysis providing foundation and machine learning generating insights.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
This document discusses various data reduction techniques including dimensionality reduction through attribute subset selection, numerosity reduction using parametric and non-parametric methods like data cube aggregation, and data compression. It describes how attribute subset selection works to find a minimum set of relevant attributes to make patterns easier to detect. Methods for attribute subset selection include forward selection, backward elimination, and bi-directional selection. Decision trees can also help identify relevant attributes. Data cube aggregation stores multidimensional summarized data to provide fast access to precomputed information.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
This document provides an overview of key aspects of data preparation and processing for data mining. It discusses the importance of domain expertise in understanding data. The goals of data preparation are identified as cleaning missing, noisy, and inconsistent data; integrating data from multiple sources; transforming data into appropriate formats; and reducing data through feature selection, sampling, and discretization. Common techniques for each step are outlined at a high level, such as binning, clustering, and regression for handling noisy data. The document emphasizes that data preparation is crucial and can require 70-80% of the effort for effective real-world data mining.
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This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. Knowlege Discovery for the Semantic Web
under Data Mining Perspective
Claudia d'Amato – University of Bari - Italy
Laura Hollink - Centrum Wiskunde & Informatica,
Amsterdam, NL
2. Knowledge Disovery: Definition
Knowledge Discovery (KD)
“the process of automatically searching large
volumes of data for patterns that can be
considered knowledge about the data” [Fay'96]
Patterns need to be:
New – Hidden in the data
Useful
Understandable
3. What is a Pattern and Knowldge?
Pattern
expression E in a given language L describing a
subset FE
of facts F. E is called pattern if it is
simpler that enumerating facts in F_E
Knowledge
awareness or understanding of facts, information,
descriptions, or skills, which is acquired through
experience or education by perceiving,
discovering, or learning
4. Knowledge Discovery
and Data Minig
KD is often related with Data Mining (DM) field
DM is one step of the "Knowledge Discovery in
Databases" process (KDD)[Fay'96]
DM is the computational process of discovering
patterns in large data sets involving methods at
the intersection of artificial intelligence,
machine learning, statistics, and databases.
DM goal: extracting information from a data
set and transforming it into an understandable
structure/representation for further use
5. What is not DM
Not all information discovery tasks are considered
to be DM.
Looking up individual records using a DBMS
Finding particular Web pages via a query to a
search engine
Tasks releted to the Information Retrieval (IR)
Nonetheless, DM techniques can been used to
improve IR systems
e.g. to create index structures for efficiently
organizing and retrieving information
6. The KDD process
Input
Data
Data Preprocessing
and Transformation
Data Mining
Interpretation
and
Evaluation
Information/
Taking Action
Data fusion (multiple sources)
Data Cleaning (noise,missing val.)
Feature Selection
Dimentionality Reduction
Data Normalization
The most labourous and
time consuming step
Filtering Patterns
Visualization
Statistical Analysis
- Hypothesis testing
- Attribute evaluation
- Comparing learned models
- Computing Confidence Intervals
CRISP-DM (Cross Industry Standard Process for Data
Mining) alternative process model developed by a
consortium of several companies
All data mining methods use induction-based learing
The knowledge
gained at the end of
the process is given
as a model/ data
generalization
7. The KDD process
Input
Data
Data Preprocessing
and Transformation
Data
Mining
Interpretation
and
Evaluation
Information/
Taking Action
Data fusion (multiple sources)
Data Cleaning (noise,missing val.)
Feature Selection
Dimentionality Reduction
Data Normalization
The most labourous and
time consuming step
Filtering Patterns
Visualization
Statistical Analysis
- Hypothesis testing
- Attribute evaluation
- Comparing learned models
- Computing Confidence Intervals
CRISP-DM (Cross Industry Standard Process for Data
Mining) alternative process model developed by a
consortium of several companies
All data mining methods use induction-based learing
The knowledge
gained at the end of
the process is given
as a model/ data
generalization
8. Data Mining Tasks...
Predictive Tasks: predict the value of a
particular attribute (called target or dependent
variable) based on the value of other attributes
(called explanatory or independent variables)
Goal: learning a model that minimizes the error
between the predicted and the true values of
the target variable
Classification → discrete target variables
Regression → continuous target variables
9. ...Data Mining Tasks...
Examples of Classification tasks
Develop a profile of a “successfull” person
Predict customers that will respond to a marketing
compain
Examples of Regression tasks
Forecasting the future price of a stock
10. … Data Mining Tasks...
Descriptive tasks: discover patterns
(correlations, clusters, trends, trajectories,
anomalies) summarizing the underlying
relationship in the data
Association Analysis: discovers (the most
interesting) patterns describing strongly
associated features in the data/relationships
among variables
Cluster Analysis: discovers groups of closely
related facts/observations. Facts belonging to the
same cluster are more similar each other than
observations belonging other clusters
11. ...Data Mining Tasks...
Examples of Association Analysis tasks
Market Basket Analysis
Discoverying interesting relationships among
retail products. To be used for:
Arrange shelf or catalog items
Identify potential cross-marketing strategies/cross-
selling opportunities
Examples of Cluster Analysis tasks
Automaticaly grouping documents/web pages
with respect to their main topic (e.g. sport,
economy...)
12. … Data Mining Tasks
Anomaly Detection: identifies facts/observations
(Outlier/change/deviation detection) having
characteristics significantly different from the rest
of the data. A good anomaly detector has a high
detection rate and a low false alarm rate.
• Example: Determine if a credit card purchase is
fraudolent → Imbalance learning set
Approaches:
Supervised: build models by using input
attributes to predict output attribute values
Unsupervised: build models/patterns without
having any output attributes
13. The KDD process
Input
Data
Data Preprocessing
and Transformation
Data Mining
Interpretation
and
Evaluation
Information/
Taking Action
Data fusion (multiple sources)
Data Cleaning (noise,missing val.)
Feature Selection
Dimentionality Reduction
Data Normalization
The most labourous and
time consuming step
Filtering Patterns
Visualization
Statistical Analysis
- Hypothesis testing
- Attribute evaluation
- Comparing learned models
- Computing Confidence Intervals
CRISP-DM (Cross Industry Standard Process for Data
Mining) alternative process model developed by a
consortium of several companies
All data mining methods use induction-based learing
The knowledge
gained at the end of
the process is given
as a model/ data
generalization
14. A closer look at the Evalaution step
Given
DM task (i.e. Classification, clustering etc.)
A particular problem for the chosen task
Several DM algorithms can be used to solve the
problem
1) How to assess the performance of an
algorithm?
2) How to compare the performance of different
algorithms solving the same problem?
16. Assessing Algorithm Performances
Components for supervised learning [Roiger'03]
Test data missing in unsupervised setting
Instances
Attributes
Data
Training
Data
Test Data
Model
Builder
Supervised
Model
Evaluation
Parameters
Performance
Measure
(Task Dependet)
Examples of Performace Measures
Classification → Predictive Accuracy
Regression → Mean Squared Error (MSE)
Clustering → Cohesion Index
Association Analysis → Rule Confidence
….....
17. Supervised Setting: Building
Training and Test Set
Necessary to predict performance bounds based
with whatever data (independent test set)
Split data into training and test set
The repeated and stratified k-fold cross-
validation is the most widly used technique
Leave-one-out or bootstrap used for small
datasets
Make a model on the training set and evaluate
it out on the test set [Witten'11]
e.g. Compute predictive accuracy/error rate
18. K-Fold Cross-validation (CV)
First step: split data into k subsets of equal size
Second step: use each subset in turn for testing, the
remainder for training
Subsets often stratified → reduces variance
Error estimates averaged to yield the overall
error estimate
Even better: repeated stratified cross-
validation
E.g. 10-fold cross-validation is repeated 15
times and results are averaged → reduces the
variance
Test set step 1 Test set step 2 …..........
19. Leave-One-Out cross-
validation
Leave-One-Out:
a particular form of cross-validation:
Set number of folds to number of training
instances
I.e., for n training instances, build classifier n
times
The results of all n judgement are averaged for
determining the final error estimate
Makes best use of the data for training
Involves no random subsampling
There's no point in repeating it → the same
result will be obtained each time
20. The bootstrap
CV uses sampling without replacement
The same instance, once selected, cannot be
selected again for a particular training/test set
Bootstrap uses sampling with replacement
Sample a dataset of n instances n times with
replacement to form a new dataset
Use this data as the training set
Use the remaining instances not occurting in the
training set for testing
Also called the 0.632 bootstrap → The training
data will contain approximately 63.2% of the total
instances
21. Estimating error
with the bootstrap
The error estimate of the true error on the test
data will be very pessimistic
Trained on just ~63% of the instances
Therefore, combine it with the resubstitution
error:
The resubstitution error (error on training data)
gets less weight than the error on the test data
Repeat the bootstrap procedure several times
with different replacement samples; average the
results
23. Comparing Algorithms Performance
Frequent question: which of two learning algorithms
performs better?
Note: this is domain dependent!
Obvious way: compare the error rates computed by the
use of k-fold CV estimates
Problem: variance in estimate on a single 10-fold CV
Variance can be reduced using repeated CV
However, we still don’t know whether the results are
reliable
24. Significance tests
Significance tests tell how confident we can be
that there really is a difference between the two
learning algorithms
Statistical hypothesis test exploited → used for
testing a statistical hypothesis
Null hypothesis: there is no significant
(“real”) difference (between the algorithms)
Alternative hypothesis: there is a difference
Measures how much evidence there is in favor
of rejecting the null hypothesis for a specified
level of significance
25. Hypothesis testing in a Nutshell...
Fix the null hypothesis
Consider the statistical assumptions about the
sample in doing the test; examples:
statistical independence
the form of the distributions of the observations
State the relevant test statistic T (what has to
be measure)
Derive the distribution of the test statistic under
the null hypothesis from the assumptions.
– i.e. the test statistic might follow a Student's t
distribution or a normal distribution.
26. ...Hypothesis testing in a Nutshell
Select a significance level (α), a probability
threshold below which the null hypothesis is
rejected. Common values: 5% and 1%.
Compute from the observations the observed
value Tobs
of the test statistic T.
Calculate the p-value (function of the observed
sample results and the assumed distribution)
If p-value <= significance level (α)
– null hypothesis rejected
– Accepted otherwise
27. Performing the Hypothesis Test for
comparing classification algorithms...
Compare two learning algorithms by comparing the
average error rate over several cross-validations
Treat the error rates computed in the CV runs as
different, independent samples from a probability
distribution
With small samples (k < 100) the mean follows
Student’s distribution with k–1 degrees of
freedom
With a large number of samples the mean follows
Normal distribution
The difference of the means (test statistic) of the
CV for each algorithm will follow the same
distribution
28. ...Performing the Hypothesis Test for
comparing classification algorithms
Question: do the two means of e.g. the 10-fold CV
estimates differ significantly?
Null Hypotheis: there is no significant difference between
the two means
Fix the confidence value (e.g. α = 5%)
Compute the value of the
test statistic, Tobs
and the p-value
on the ground of the determined distribution (paired t-test
for the case of Student distribution and same CV,
unpaired t-test for the case of different CV)
Accept or reject the null hypothesis on the ground of the
computed p-value
29. DM methods and SW: a closer look
Classical DM algorithms originally developed
for propositional representation
Some upgrades to (multi-)relational and graph
representations defined
Semantic Web: characterized by
Rich/expressive representations (RDFS, OWL)
– How to cope with them when applying DM
algorithms?
Open world Assumpion (OWA)
– DM algorithms grounded on CWA
– Are metrics for classical DM tasks still applicable?
30. Exploiting DM methods in SW...
● Approximate inductive instance retrieval
● assess the class membership of the individuals in a
KB w.r.t. the query concept [Fanizzi'12]
● Link Prediction
● Given an individual and a role R, predict the other
individuals a is in R relation with [Minervini'14]
● Regarded as a classification problem →
(semi-)automatic ontology population
31. ...Exploiting DM mthods in SW...
Automatic concept drift and novelty detection
[Fanizzi'09]
regarded as a (conceptual) clustering problem
change of a concept towards a more
general/specific one w.r.t. the evidence provided
by new annotated individuals
almost all Worker work for more than 10 hours per
days → HardWorker
isolated cluster may require to be defined through
new emerging concepts to be added to ontology
subset of Worker employed in a company →
Employee
subset of Worker working for several companies →
Free-lance
32. ...Exploiting DM mthods in SW
Semi-automatic ontology enrichment
[d'Amato'10,Völker'11]
regarded as pattern discovery problem
exploiting the evidence coming from the data →
discovering hidden knowledge patterns in the form of
relational association rules
new axioms may be suggested → existing ontologies
can be extended
34. Associative Analysis:
the Pattern Discovery Task
Problem Definition:
Given a dataset find
● all possible hidden pattern in the form of
Association Rule (AR)
● having support and confidence greater than the
minimum thresholds
Def.: An AR is an implication expression of the
form X → Y where X and Y are disjoint itemsets
An AR expresses a co-occurrence relationship
between the items in the antecedent and the
concequence and not a causality relationship
35. Basic Definitions
An itemset is a finite set of assignments of the form
{A1
= a1
, …, Am
= am
} where Ai
are attributes of the
dataset and ai
the corresponding values
The support of an itemset is the number of
istances/tuples in the dataset containing it.
Similarily, support of a rule is s(X → Y ) = |(X Y)|;
The confidence of a rule provides how frequently
items in the consequence appear in
instances/tuples containing the atencedent
c(X → Y ) = |(X union Y)| / |(X)| (seen as p(Y|X) )
36. Discoverying Association Rules:
General Approach
Articulated in two main steps [Agrawal'93, Tan'06]:
1. Frequent Patterns Generation/Discovery
(generally in the form of itemsets) wrt a
minimum frequency (support) threshold
Apriori algortihm → The most well known
algorithm
the most expensive computation;
2. Rule Generation
Extraction of all the high-confidence association
rules from the discovered frequent patterns.
37. Apriori Algortihm: Key Aspects
Uses a level-wise generate-and-test approach
It is grounded on the non-monotonic property
of the support of an itemset
The support of an itemset never exceed the
support of its subsets
Basic principle:
if an itemset is frequent → all its subsets must
also be frequent
If an itemset is infrequent → all its supersets must
be infrequent too
Allow to sensibly cut the search space
38. Apriori Algorithm in a Nutshell
Goal: Finding the frequent itemsets ↔ the sets of
items that satisfying the min support threshold
Iteratively find frequent itemsets with lenght from
1 to k (k-itemset)
Given a set Lk-1
of frequent (k-1)itemset, join Lk-1
with itself to obain Lk
the candidate k-itemsets
Prune items in Lk
that are not frequent (Apriori
principle)
If Lk
is not empty, generate the next candidate
(k+1)itemset until the frequent itemset is empty
40. Apriori Algorithm: Example
Itemset Prune
Infrequent
{I1,I2,I3} No
{I1,I2,I5} No
{I1,I2,I4} Yes {I1,I4}
{I1,I3,I5} Yes {I3,I5}
{I2,I3,I4} Yes {I3,I4}
{I2,I3,I5} Yes {I3,I5}
{I2,I4,I5} Yes {I4,I5}
Output After Pruning
L4
Min.
Supp. 2
Pruning
Join for
candidate
generation
Itemset Sup.
Count
{I1,I2} 4
{I1,I3} 4
{I1,I5} 2
{I2,I3} 4
{I2,I4} 2
{I2,I5} 2
Apply Apriori
principle
Itemset Sup.
Count
{I1,I2,I3} 2
{I1,I2,I5} 2
Join for
candidate
generation
L3
Output After Pruning
Itemset Prune
Infrequent
{I1,I2,I3,I5} Yes i.e.
{I3,I5}
Empty
Set
STOP
41. Generating ARs
from frequent itemsets
● For each frequent itemset “I” generate all non-
empty subsets S of I
● For every non empty subset S of I, compute the
rule r := “S → (I-S)”
● If conf(r) > = min. confidence then output r
42. Genrating ARs: Example...
Given:
L = { {I1}, {I2}, {I3}, {I4}, {I5}, {I1,I2}, {I1,I3}, {I1,I5},
{I2,I3}, {I2,I4}, {I2,I5}, {I1,I2,I3}, {I1,I2,I5} }.
Minimum confidence threshold , say 70%.
Lets take l = {I1,I2,I5}.
All nonempty subsets are {I1,I2}, {I1,I5}, {I2,I5},
{I1}, {I2}, {I5}.
The resulting ARs and their confidence are:
R1: I1 AND I2 →I5
Conf(R1) = supp{I1,I2,I5}/supp{I1,I2} = 2/4 = 50% REJECTED
43. ...Generating ARs: Example...
Min. Conf. Threshold 70%; l = {I1,I2,I5}.
All nonempty subsets are {I1,I2}, {I1,I5}, {I2,I5},
{I1}, {I2}, {I5}.
The resulting ARs and their confidence are:
R2: I1 AND I5 →I2
Conf(R2) = supp{I1,I2,I5}/supp{I1,I5} = 2/2 = 100% RETURNED
R3: I2 AND I5 → I1
Conf(R3) = sc{I1,I2,I5}/sc{I2,I5} = 2/2 = 100% RETURNED
R4: I1 → I2 AND I5
Conf(R4) = sc{I1,I2,I5}/sc{I1} = 2/6 = 33% REJECTED
44. ...Genrating ARs: Example
Min. Conf. Threshold 70%; l = {I1,I2,I5}.
All nonempty subsets: {I1,I2}, {I1,I5}, {I2,I5},
{I1}, {I2}, {I5}.
The resulting ARs and their confidence are:
R5: I2 → I1 AND I5
Conf(R5) = sc{I1,I2,I5}/sc{I2} = 2/7 = 29% REJECTED
R6: I5 → I1 AND I2
Conf(R6) = sc{I1,I2,I5}/ {I5} = 2/2 = 100% RETURNED
Similarily for the other sets I in L (Note: it does not make sense to
consider an itemset made by just one element i.e. {I1} )
45. Identifying Representative Itemsets
When the number of discovered frequent itemsets
is very high, it could be useful to identify a
representative set of itemsets from which all
other patters may be derived
Maximal Frequent Itemset: is a frequent
itemset for which none of its immediate
supersets are frequent
Closed Frequent Itemset: is a frequent itemset
for which none of its immediate supersets has
exactly its same support count → used for
removing redundant rules
46. On improving Discovery of ARs
Apriori algorithm may degrade significantly for
dense datasets
FP-growth algorithm outperforms
Does not use the generate-and-test approach
Encodes the dataset in a compact data
structure (FP-Tree) and extract frequent
itemsets directly from it
Usage of additional interenstingness metrics
(besides support and confidence) (see [Tan'06])
Lift, Interest Factor, correlation, IS Measure
47. Frequent Graph Patterns
Frequent Graph Patterns are subgraphs that
are found from a collection of graphs or a
single massive graph, with a frequency no less
than a specifed support threshold
– Exploited for facilitating indexing and query
processing
A graph g is a subgraph of another graph g' if
there exists a subgraph isomorphism from g to
g' denoted by g g'. g' is called a supergraph
of g
48. Discoverying Frequent Graph
Patterns
Apriori-based and pattern-growth appraoches
have been formally defined
Problems:
Giving suitable definitions for support and
conficence for frequent subgraph mining
problem
Even more complicate for the case of a single
large graph [see Aggarwal'10 sect. 2.5]
Developed appraoches revealed infeasible in
practice
49. Graph Mining: Current Approaches
Methods for mining [Aggarwal'10, ch. 3, 4]:
Significant (optimal) subgraphs according to an
objective function
In a timely way by accessing only a small subset
of promising subgraphs
Representative (orthogonal) subgraphs by
exploiting a notion of similarity
Avoid generating the complete set of frequent
subgraphs while presenting only a set of
interesting subgraph patterns
50. Pattern Discovery on RDF data for
Making Predictions
Proposed a framework for discoverying ARs from
RDF data [Galarraga'13]
Inspired to ILP appraoches for discovering Ars
from clausal representation
Exploits discovered ARs for making new role
preditions
Takes into account the underlying OWA
Proposes new metrics for evaluating the
prediction results considering the OWA
51. Research Task: Goal
Moving from [Galarraga'13], define a method for
Discoverying ARs from ontological KBs
Exploit the underlying semantics
Predict Concept Assertions besides of
Relationships
Take into account the OWA
Analyse the proposed metrics for the
evaluation and assess if new/additional metrics
are necessary
Optional: would it be possible to make
additional usages of discovered rules?
52. Research Task: Expected Output
4 Mins Presentation summarizing
● [Galarraga'13] approach (only one group 5 min
presentation)
● Proposed solution and its value added/advace
with respect to [Galarraga'13]
● Outcome on the analysis of the metrics and
potential new proposals
Laura gives details on how groups will work in
practice
53. References...
[Fay'96] U. Fayyad, G. Piatetsky-Shapiro, P.
Smyth. From Data Mining to Knowledge
Discovery: An Overview. Advances in Knowledge
Discovery and Data Mining, MIT Press, 1996.
[Agrawal'93] R. Agrawal, T. Imielinski, and A. N.
Swami. Mining association rules between sets of
items in large databases. Proc. of Int. Conf. on
Management of Data, p. 207–216. ACM, 1993
[d'Amato'10] C. d'Amato, N. Fanizzi, F. Esposito:
Inductive learning for the Semantic Web: What
does it buy? Semantic Web 1(1-2): 53-59 (2010)
[Völker'11] J. Völker, M. Niepert: Statistical
Schema Induction. ESWC (1) 2011: 124-138
54. ...References...
[Tan'06] P.N. Tan, M. Steinbach, V. Kumar. Introduction
to Data Mining. Ch. 6 Pearson, 2006 . http://www-
users.cs.umn.edu/~kumar/dmbook/ch6.pdf
[Aggarwal'10] C. Aggarwal, H. Wang. Managing and
Mining Graph Data. Springer, 2010
[Witten'11] I.H. Witten, E. Frank. Data Mining: Practical
Machine Learning Tool and Techiques with Java
Implementations. Ch. 5. Morgan-Kaufmann, 2011 (3rd
Edition)
[Minervini'14] P. Minervini, C. d'Amato, N. Fanizzi, F.
Esposito: Adaptive Knowledge Propagation in Web
Ontologies. Proc. of EKAW Conferece. Springer. pp.
304-319, 2014.
55. ...References
[Roiger'03] R.J. Roiger, M.W. Geatz. Data
Mining. A Tutorial-Based Primer. Addison Wesley,
2003
[Galárraga'13] L. Galárraga, C. Teflioudi, F.
Suchanek, K. Hose. AMIE: Association Rule
Mining under Incomplete Evidence in Ontological
Knowledge Bases. Proc. of WWW 2013.
http://luisgalarraga.de/docs/amie.pdf
[Fanizzi'09] N. Fanizzi, C. d'Amato, F. Esposito:
Metric-based stochastic conceptual clustering for
ontologies. Inf. Syst. 34(8): 792-806, 2009