17CS651 Data Mining and Data Warehousing syllabus for IS

Data Warehousing & modeling:

Basic Concepts: Data Warehousing: A multitier Architecture, Data warehouse models: Enterprise warehouse,Data mart and virtual warehouse, Extraction, Transformation and loading, Data Cube: A multidimensional data model, Stars, Snowflakes and Fact constellations: Schemas for multidimensional Data models, Dimensions: The role of concept Hierarchies, Measures: Their Categorization and computation, Typical OLAP Operations.

Data warehouse implementation & Data mining:

Efficient Data Cube computation: An overview, Indexing OLAP Data: Bitmap index and join index, Efficient processing of OLAP Queries, OLAP server Architecture ROLAP versus MOLAP Versus HOLAP.: Introduction: What is data mining, Challenges, Data Mining Tasks, Data: Types of Data, Data Quality, Data Preprocessing, Measures of Similarity and Dissimilarity,

Association Analysis:

Association Analysis: Problem Definition, Frequent Item set Generation, Rule generation. Alternative Methods for Generating Frequent Item sets, FP-Growth Algorithm, Evaluation of Association Patterns.

Classification:

Decision Trees Induction,Method for Comparing Classifiers, Rule Based Classifiers, Nearest Neighbor Classifiers,Bayesian Classifiers.

Clustering Analysis:

Overview, K-Means, Agglomerative Hierarchical Clustering, DBSCAN, Cluster Evaluation, Density-Based Clustering, Graph- Based Clustering, Scalable Clustering Algorithms.

Course outcomes:

The students should be able to:

• Understands data mining problems and implement the data warehouse
• Demonstrate the association rules for a given data pattern.
• Discuss between classification and clustering solution.

Question paper pattern:

• The question paper will have TEN questions.
• There will be TWO questions from each module.
• Each question will have questions covering all the topics under a module.
• The students will have to answer FIVE full questions, selecting ONE full question from each module.

Text Books:

1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson, First impression,2014.

2. Jiawei Han, MichelineKamber, Jian Pei: Data Mining -Concepts and Techniques, 3rd Edition,Morgan Kaufmann Publisher, 2012.

Reference Books:

1. Sam Anahory, Dennis Murray: Data Warehousing in the Real World, Pearson,Tenth Impression,2012.

2. Michael.J.Berry,Gordon.S.Linoff: Mastering Data Mining , Wiley Edition, second edtion,2012.