DATA MINING AND DATA WAREHOUSING (18CS641)

DATA MINING AND DATA WAREHOUSING

SEMESTER – VI
Course Code-18CS641
CIE Marks-40
Number of Contact Hours/Week-3:0:0
SEE Marks-60
Total Number of Contact Hours-40
Exam Hours-03
CREDITS –3

Course Learning Objectives: This course (18CS641) will enable students to:

 Define multi-dimensional data models.
 Explain rules related to association, classification and clustering analysis.
 Compare and contrast between different classification and clustering algorithms

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
RBT: L1, L2, L3

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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
RBT: L1, L2, L3

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Module 3

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.
Textbook 1: Ch 6.1 to 6.7 (Excluding 6.4)
RBT: L1, L2, L3

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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
RBT: L1, L2, L3

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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
RBT: L1, L2, L3

Click here to download Module-5

Important Links:

1. Click here to download the complete 5 modules handwritten Notes

Course Outcomes: The student will be able to :

 Identify data mining problems and implement the data warehouse
 Write association rules for a given data pattern.
 Choose between classification and clustering solution.

Question Paper Pattern:

 The question paper will have ten questions.
 Each full Question consisting of 20 marks
 There will be 2 full questions (with a maximum of four sub questions) from each module.
 Each full question will have sub questions covering all the topics under a module.
 The students will have to answer 5 full questions, selecting one full question from each module.

Textbooks:

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