Data Warehousing and Data Mining Cse 6th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective I) detail syllabus for Computer Science & Engineering (Cse), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (CS8075), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other cse 6th sem syllabus for be 2017 regulation anna univ you can visit Cse 6th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective I subjects do refer to Professional Elective I. The detail syllabus for data warehousing and data mining is as follows.
Course Objective:
- To understand data warehouse concepts, architecture, business analysis and tools
- To understand data pre-processing and data visualization techniques
- To study algorithms for finding hidden and interesting patterns in data
- To understand and apply various classification and clustering techniques using tools.
Unit I
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Unit II
Data Mining – Introduction
Introduction to Data Mining Systems – Knowledge Discovery Process – Data Mining Techniques – Issues – applications- Data Objects and attribute types, Statistical description of data, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures.
Unit III
Data Mining – Frequent Pattern Analysis
Mining Frequent Patterns, Associations and Correlations – Mining Methods- Pattern Evaluation Method – Pattern Mining in Multilevel, Multi Dimensional Space – Constraint Based Frequent Pattern Mining, Classification using Frequent Patterns
Unit IV
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Unit V
Weka Tool
Datasets – Introduction, Iris plants database, Breast cancer database, Auto imports database -Introduction to WEKA, The Explorer – Getting started, Exploring the explorer, Learning algorithms, Clustering algorithms, Association-rule learners.
Course Outcome:
Upon completion of the course, the students should be able to:
- Design a Data warehouse system and perform business analysis with OLAP tools.
- Apply suitable pre-processing and visualization techniques for data analysis
- Apply frequent pattern and association rule mining techniques for data analysis
- Apply appropriate classification and clustering techniques for data analysis
Text Books:
- Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, Third Edition, Elsevier, 2012.
References:
- Alex Berson and Stephen J.Smith, Data Warehousing, Data Mining and OLAP, Tata McGraw – Hill Edition, 35th Reprint 2016.
- K.P. Soman, Shyam Diwakar and V. Ajay, Insight into Data Mining Theory and Practice, Eastern Economy Edition, Prentice Hall of India, 2006.
- Ian H.Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, Second Edition.
For detail syllabus of all other subjects of BE Cse, 2017 regulation do visit Cse 6th Sem syllabus for 2017 Regulation.
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