8 IT 1-DATA MININIG AND WAREHOUSING |
Units: 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.
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Units: II |
Concept Description: Definition, Data Generalization, Analytical Characterization,
Analysis of attribute relevance, Mining Class comparisons, 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. |
Units: III |
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
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Units: IV |
Data Warehousing: Overview, Definition, Delivery Process, Difference between
Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes,
Stars, Snow Flakes, Fact Constellations, Concept hierarchy, Process Architecture, 3 Tier
Architecture, Data Marting.
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Units: V |
Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP
Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and
Recovery, Tuning Data Warehouse, Testing Data Warehouse.
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