Data Mining
SEM VI BCA-604 (C)
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Rs.80.00
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Data Mining is a Simple version for T.Y.B.C.A. students of our
Prashant Publication.
This text is in accordance with the new syllabus CBCS-2023
recommended by the Kavayitri Bahainabai Chaudhari North Maharashtra
University, Jalgaon, which has been serving the need of T.Y.B.C.A.
Computer Science students from various colleges. This text is also
useful for the student of Engineering, B.Sc. (Information Technology
and Computer Science), M.Sc, M.C.A. B.B.M., M.B.M. other different
Computer courses.
We are extremely grateful to Prof. Dr. S.R.Kolhe, Chairman, Board
of Studies, and all BOS members of Kavayitri Bahinabai Chaudhari North
Maharashtra University, Jalgaon for his valuable guidance.
We are grateful to Prof. Sanjay E. Pate of Nanasaheb Yashwantrao
Narayanrao Chavan Arts, Science, and Commerce College, Chalisgaon for
coordinating all authors and publication team.
We are obligated to Principals and Librarians and staff of respective
colleges for their encouragement.
We are very much thankful to Shri. Rangrao Patil of Prashant
Publications, who has shown extreme co-operation during the preparation
of this book, for getting the book published in time and providing an
opportunity to be a part of this book.
We shall be glad to receive any suggestions for improving the contents
of this book.
UNIT – 1……………………………………………………………………………………………..7
Introduction to Data Warehousing
1.1 Introduction
1.2 What is Data Warehouse? Definition
1.3 Multidimensional Data Model
1.4 OLAP Operations
1.5 Warehouse Schema
1.6 Data Warehouse Architecture
1.7 Warehouse Server
1.8 Metadata
1.9 OLAP Engine
1.10 Data Warehouse Backend Process. .
UNIT – 2……………………………………………………………………………………………19
Introduction to Data Mining
2.1 What is Data Mining?
2.2 History of Data Mining
2.3 Types of Data
2.4 Data Mining Techniques
2.5 Data Mining Implementation Process
2.6 Data Mining vs Machine Learning
UNIT – 3……………………………………………………………………………………………29
Basics of Data Mining and Models
3.1 Introduction to Data Mining Functionalities
3.2 Issues in Data Mining
3.3 Data Mining Architecture
3.4 Data Mining Models
3.5 Types of data mining models
3.6 Interestingness of Patterns – Classification of Data Mining Systems – Data
Mining Task.
UNIT – 4……………………………………………………………………………………………38
Association Rule Mining
4.1 Mining Frequent Patterns
4.2 Associations and correlations
4.3 Mining Methods4.4 Mining Various Kinds of Association Rules
4.5 Correlation Analysis
4.6 Constraint Based Association Mining
UNIT – 5……………………………………………………………………………………………53
Classification of Data Mining
5.1 Classification and Prediction – Basic concepts
5.2 Decision Tree Induction
5.3 Bayesian Classification
5.4 Rule Based classification
5.5 Classification by Back propagation
5.6 Support Vector Machines
UNIT – 6……………………………………………………………………………………………69
Clustering and Applications
6.1 Cluster analysis
6.2 Categorization of Major Clustering methods
6.3 K-means partitioning methods
6.4 Hierarchical Methods- Data Mining Applications
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