Data Warehouse & Data Mining

Syllabus:

UNIT-1: Data Warehouse and OLAP Technology: What is Data Warehouse, Data Warehouse Architecture, Multidimensional Data Model, OLAP Operations on Multidimensional Data cube computation: Multiway Array Aggregation, BUC.

UNIT-2: Introduction to Data Mining: Fundamentals of data mining & Data Mining Functionalities, KDD Process, Data Mining Task Primitives & Major issues in Data Mining. Needs for Preprocessing the Data & Data Cleaning, Needs for Preprocessing the Data & Data Cleaning, Data Integration and Transformation, Data Integration and Transformation, Data Reduction.

UNIT-3: Mining Frequent Pattern, Associations and Correlations: Basic Concepts, Efficient and Scalable Frequent Itemset Mining Methods, various kinds of Association Rules.

Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification.

UNIT-4: Cluster Analysis Introduction: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods & k-MEANS, PAM, Hierarchical Methods-BIRCH, Density-Based Methods-DBSCAN.

UNIT-5: Pattern Discovery in real world data (FLIPPED CLASSROOM): Mining Time-Series Data, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web, Data Mining Applications.

Course Outcomes:

Students will be able to:

• CO1: Design a data mart or data warehouse for any organization

• CO2: Create association rules and classification models to different data sets

• CO3: Evaluate different clustering algorithms .

• CO4: Apply Data mining techniques on web data, spatial data, temporal data