BITM Data Warehousing and Data Mining

bitmsemester 7Elective

This course covers different concepts of data warehousing and data mining including data mining, data warehousing and OLAP, and different concepts and algorithms of pattern mining, classification, clustering and outlier detection.

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Chapter Wise Notes Question Banks Syllabus Chapter Wise Questions Viva Practical

Unit 1:Introduction

Data Mining and Knowledge Discovery. Mining Various Kinds of Knowledge. Data Mining Applications. Data Types. Statistics of Data. Similarity and Distance Measures. Data Quality, Data Cleaning, and Data Integration. Data Transformation. Dimensionality Reduction.

Unit 2:Data Warehousing and Online Analytical Processing

Data Warehouse. Data Warehouse Modeling: Schema and Measures. OLAP Operations. Data Cube Computation. Data Cube Computation Methods.

Unit 3:Pattern Mining

Basic Concepts. Frequent Itemsets, Closed Itemsets, and Association Rules. Frequent Itemset Mining Methods: Apriori and Pattern-Growth Approach. Generating Association Rules from Frequent Itemsets. Pattern Evaluation Methods.

Unit 4:Classification

Basic Concepts. Decision Tree Induction. Bayes Classification Methods. Lazy Learners. Linear Classifiers. Model Evaluation and Selection. Techniques to Improve Classification Accuracy.

Unit 5:Cluster Analysis

Cluster Analysis. Partitioning Methods (k-Means, k-Mediods and k-Modes). Hierarchical Methods (Agglomerative and Divisive). Desisty-Based (DBSCAN). Evaluation of Clustering.

Unit 6:Outlier Detection

Basic Concepts. Statistical Approaches. Proximity-Based Approaches. Reconstruction-Based Approaches. Clustering-Based Approaches. Classification-Based Approaches.