Syllabus

CSE4705 - Data Warehousing And Data Mining 

Credits: [ L -T -P -C ]:[3-0-0-3]

Pre-requisites/Exposure: --

Co-requisites :---

Course Content

Unit I  Introduction and Data Preprocessing                                                                                  8 hours

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, Numerosity Reduction, Data Discretization and Concept hierarchy generation

Unit II Data Warehouse and OLAP Technology                                                                             6 hours

What is data warehouse, Multidimentional data model, Data warehouse architecture, Data warehouse implementation, From data warehouse to data mining

Unit III  Association Rule Mining                                                                                                      4 hours

What is an association rule, Support and confidence measures, upward and downward closure property, frequent itemsets, closed frequent itemsets, maximal frequent itemsets, border set, Mining various kinds of association rules, From Association rule mining to correlation analysis, Constraint based association mining.

Unit IV Classification                                                                                                                              7 hours

Classification, prediction, issues regarding classification and prediction, comparing classification and prediction methods, classification by decision tree induction, Attribute selection measures: Information gain, Gain ratio, Gini Index, Tree pruning, Bayesian classification, Naïve Bayesian classification.

Unit V  Clustering                                                                                                                                      5 hours

Clustering, Cluster analysis, Types of data in cluster analysis: Interval scaled variables, Binary variables, categorical, ordinal, and ratio scaled variables, variables of mixed types, Major clustering methods: Partitioning methods, Hierarchical methods, Density based methods, Grid based methods, Model based methods, Clustering high dimensional data, Constraint based clustering, outlier analysis

Modes of Evaluation: Quiz/Assignment/ Seminar/Written Examination

 Examination Scheme:

 Relationship between the Course Outcomes (COs) and Program Outcomes (POs)