Data Warehousing and Data Mining
Subject Overview
The third Elective-I option, covering data warehouse architecture and OLAP, data preprocessing and multidimensional modeling (star/snowflake schemas), and data mining techniques — association rule mining, Bayesian and other classification methods, and major clustering approaches. A 4-credit elective theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: Data warehousing fundamentals
Data warehouse components and architecture, mapping to multiprocessor architecture, need for data warehousing, operational vs informational data stores, OLAP vs OLTP and their differences, OLAP operations
Unit 2: Building a data warehouse
Design, technical and implementation considerations, data preprocessing (summarization, cleaning, transformation), ETL process, multidimensional data model, star/snowflake/fact-constellation schemas, data warehouse architecture and design, OLAP three-tier architecture, cube computation, data marts
Unit 3: Data mining fundamentals
Data mining functionalities, mining different kinds of data, classification vs clustering, predictive vs descriptive mining, association rule mining, market basket analysis, the Apriori algorithm
Unit 4: Classification and clustering
Bayesian classification (Bayes theorem, Bayesian belief networks, Naive Bayes), classification by back-propagation, k-Nearest Neighbour, Support Vector Machine, linear and multiple regression prediction, clustering data types, partitioning methods (K-Means, K-Medoids), hierarchical agglomerative methods, DBSCAN, using Weka/Python for implementation