data mining & data warehousing

Semester : VI

Course Code : 18CS641

CIE Marks : 40 SEE Marks : 60

DATA MINING AND DATA WAREHOUSING
18CS641

SYLLABUS

Module-1

Data Warehousing & modeling: Basic Concepts: Data Warehousing: A multitier Architecture, Data warehouse models: Enterprise warehouse, Datamart 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
Textbook 2: Ch.4.1,4.2

Module-2

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.
Textbook 2: Ch.4.4
Textbook 1: Ch.1.1,1.2,1.4, 2.1 to 2.4

Module-3

Association Analysis: Association Analysis: Problem Definition, Frequent Item set Generation, Rule generation. Alternative Methods for Generating Frequent Itemsets, FP-Growth Algorithm, Evaluation of Association Patterns.
Textbook 1: Ch 6.1 to 6.7 (Excluding 6.4)

Module-4

Classification: Decision Trees Induction, Method for Comparing Classifiers, Rule Based Classifiers, Nearest Neighbor Classifiers, Bayesian Classifiers.
Textbook 1: Ch 4.3,4.6,5.1,5.2,5.3

Module-5

Clustering Analysis: Overview, K-Means, Agglomerative Hierarchical Clustering, DBSCAN, Cluster Evaluation, Density-Based Clustering, Graph-Based Clustering, Scalable Clustering Algorithms.
Textbook 1: Ch 8.1 to 8.5, 9.3 to 9.5