UNIT-V Clustering: Clustering Overview, A Categorization of Major Clustering Methods, partitioning methods, hierarchical methods,, partitioning clustering-k-means algorithm, pam algorithm hierarchical clustering-agglomerative methods and divisive methods, Basic Agglomerative Hierarchical Clustering Algorithm, Key Issues in Hierarchical Clustering, Strengths and Weakness,Outlier Detection.ĭata Warehousing and Mining PDF Data Warehousing and Mining PDFĭata Warehousing and Mining Question Paperĭata Warehousing and Mining Questions and Answers.UNIT-IV Classification: Problem Definition, General Approaches to solving a classification problem, Evaluation of Classifiers, Classification techniques, Decision Trees-Decision tree Construction, Methods for Expressing attribute test conditions, Measures for Selecting the Best Split, Algorithm for Decision tree Induction Naive-Bayes Classifier, Bayesian Belief Networks K- Nearest neighbor classification-Algorithm and Characteristics, prediction: Accuracy and Error measures, Evaluating the accuracy of a classifier or a predictor, Ensemble methods.UNIT-III Association Rules: Problem Definition, Frequent Item Set Generation, The APRIORI Principle, Support and Confidence Measures, Association Rule Generation APRIOIRI Algorithm, The Partition Algorithms, FP-Growth Algorithms, Compact Representation of Frequent Item Set- Maximal Frequent Item Set, Closed Frequent Item Set.Data Preprocessing: Need for Preprocessing the Data, Data Cleaning, Data Integration &Transformation, Data Reduction, Discretization and Concept Hierarchy Generation. UNIT-II Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or Data Warehouse System, Major issues in Data Mining.UNIT-I Data warehouse: Introduction to Data warehouse, Difference between operational database systems and data warehouses, Data warehouse Characteristics, Data warehouse Architecture and its Components, Extraction-Transformation-Loading, Logical(Multi-Dimensional), Data Modeling, Schema Design, Star and Snow-Flake Schema, Fact Constellation, Fact Table, Fully Addictive, Semi-Addictive, Non-Addictive Measures Fact-Less-Facts, Dimension Table Characteristics OLAP Cube, OLAP Operations, OLAP Server Architecture-ROLAP, MOLAP and HOLAP.You can download the syllabus in Data Warehousing and Mining pdf form. 5 Data Warehousing and Mining Question Paperĭata Warehousing and Mining Notes can be downloaded in Data Warehousing and Mining pdf from the below article.Ī detailed Data Warehousing and Mining syllabus as prescribed by various Universities and colleges in India are as under.4 Data Warehousing and Mining Questions and Answers.
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