Descriptors/Topics: The purpose of this course is to introduce the basic data mining technologies and their use for business intelligence.- DM and KDD process Integration of a data mining system with a database or a data warehousing understanding, Supervised and unsupervised learning. BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis – data cube representations, Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP versus MOLAP - defining schemas: Stars, snowflakes and fact constellations, case studies.
- What kinds of data can be mined, Data Cleaning: Missing Values, Noisy Data,(Binning, Clustering, Regression),Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, case studies.
- Association rules: Introduction to market basket analysis, Large Item sets, Basic APRIORI AND FP Tree Algorithms Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitioned Algorithms. Hierarchical Clustering Based Methods-DBSCAN, case studies.
- What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian Classification, Classification by Back propagation, K-nearest neighbor classifiers, support vector machine, regression (linear and logistic regression) , case studies.
- BI Architecture, Introduction to Business analytical tool (Power BI, LIS) spread sheets, concept of dashboard, OLAP, decision engineering, Data mining for business Applications like Balanced Scorecard, Fraud Detection, Click stream Mining, Market Segmentation, Retail industry, Telecommunications Industry, Banking & Finance and CRM etc. case studies.
