CS-042 : Data Warehousing & Mining Books & References
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Unit-I
Overview, Motivation(for Data Mining),Data Mining-Definition & Functionalities, Data Processing, Form of Data Preprocessing, Data Cleaning: Missing Values, Noisy Data,(Binning, Clustering, Regression, Computer and Human inspection),Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Clustering, Discretization and Concept hierarchy generation.
Unit-II
Concept Description:- Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisions, Statistical measures in large Databases. Measuring Central Tendency, Measuring Dispersion of Data, Graph Displays of Basic Statistical class Description, Mining Association Rules in Large Databases, Association rule mining, mining Single-Dimensional Boolean Association rules from Transactional Databases– Apriori Algorithm, Mining Multilevel Association rules from Transaction Databases and Mining Multi-Dimensional Association rules from Relational Databases.
Unit-III
Classification and Predictions: What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian Classification, Classification by Back propagation, Multilayer feed-forward Neural Network, Back propagation Algorithm, Classification methods Knearest neighbor classifiers, Genetic Algorithm.Cluster Analysis:Data types in cluster analysis, Categories of clustering methods,Partitioning methods.Hierarchical Clustering- CURE and Chameleon, Density Based Methods-DBSCAN,OPTICS, Grid Based Methods- STING, CLIQUE, Model Based Method –Statistical Approach, Neural Network approach, Outlier Analysis
Unit-IV
Data Warehousing: Overview, Definition, Delivery Process, Difference between DatabaseSystem and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, SnowFlakes, Fact Constellations, Concept hierarchy, Process Architecture, 3 Tier Architecture,Data Marting.
Unit-V
Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAPServers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery,Tuning Data Warehouse, Testing Data Warehouse.
Books:
1. M.H.Dunham,”Data Mining:Introductory and Advanced Topics” Pearson Education
2. Jiawei Han, Micheline Kamber, ”Data Mining Concepts & Techniques” Elsevier
3. Sam Anahory, Dennis Murray, “Data Warehousing in the Real World : A Practical Guide
for Building Decision Support Systems, 1/e “ Pearson Education
4. Mallach,”Data Warehousing System”,McGraw –Hill
Unit-I
Overview, Motivation(for Data Mining),Data Mining-Definition & Functionalities, Data Processing, Form of Data Preprocessing, Data Cleaning: Missing Values, Noisy Data,(Binning, Clustering, Regression, Computer and Human inspection),Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Clustering, Discretization and Concept hierarchy generation.
Unit-II
Concept Description:- Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisions, Statistical measures in large Databases. Measuring Central Tendency, Measuring Dispersion of Data, Graph Displays of Basic Statistical class Description, Mining Association Rules in Large Databases, Association rule mining, mining Single-Dimensional Boolean Association rules from Transactional Databases– Apriori Algorithm, Mining Multilevel Association rules from Transaction Databases and Mining Multi-Dimensional Association rules from Relational Databases.
Unit-III
Classification and Predictions: What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian Classification, Classification by Back propagation, Multilayer feed-forward Neural Network, Back propagation Algorithm, Classification methods Knearest neighbor classifiers, Genetic Algorithm.Cluster Analysis:Data types in cluster analysis, Categories of clustering methods,Partitioning methods.Hierarchical Clustering- CURE and Chameleon, Density Based Methods-DBSCAN,OPTICS, Grid Based Methods- STING, CLIQUE, Model Based Method –Statistical Approach, Neural Network approach, Outlier Analysis
Unit-IV
Data Warehousing: Overview, Definition, Delivery Process, Difference between DatabaseSystem and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, SnowFlakes, Fact Constellations, Concept hierarchy, Process Architecture, 3 Tier Architecture,Data Marting.
Unit-V
Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAPServers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery,Tuning Data Warehouse, Testing Data Warehouse.
Books:
1. M.H.Dunham,”Data Mining:Introductory and Advanced Topics” Pearson Education
2. Jiawei Han, Micheline Kamber, ”Data Mining Concepts & Techniques” Elsevier
3. Sam Anahory, Dennis Murray, “Data Warehousing in the Real World : A Practical Guide
for Building Decision Support Systems, 1/e “ Pearson Education
4. Mallach,”Data Warehousing System”,McGraw –Hill
