DATA MINING

Paper Code: 
MCS 425B
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course introduces basic concepts, tasks, methods, and techniques in data mining. The emphasis is on various data mining problems and their solutions. Students will develop an understanding of the data mining process and issues, learn various techniques for data mining, and apply the techniques in solving data mining problems using data mining tools and systems.

12.00
Unit I: 
Introduction:

Fundamentals of data mining, DataMining Functionalities, Classification of Data Mining systems, Major issues in Data Mining, Data Warehouse and OLAP Technology, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, Data Cube Technology, Data Warehousing to Data Mining.

12.00
Unit II: 
Data Mining Primitives, Languages, and System Architectures:

Data Mining Primitives, Data Mining Query Languages, Designing Graphical User Interfaces Based on a Data Mining Query Language Architectures.

12.00
Unit III: 
Mining Association Rules in Large Databases:

Association Rule Mining, Mining Single-Dimensional Boolean Association Rules from Transactional Databases, Mining Multilevel Association Rules from Transaction Databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.

12.00
Unit IV: 
Classification, Clustering, and Prediction:

Issues Regarding Classification and Prediction,Classification by Decision Tree Induction, Bayesian Classification, Classification byBack propagation, Classification Based on Concepts from Association Rule Mining,Other Classification Methods, Prediction, Classifier Accuracy. Cluster Analysis Introduction: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis.

 

12.00
Unit V: 
Mining Complex Types of Data:

Multidimensional Analysis and Descriptive Mining of Complex, Data Objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Time-Series and Sequence Data, Mining Text Databases, Mining the World Wide Web.
 

ESSENTIAL READINGS: 

1.Jian Pei & Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd Edition, Nov.2005

2.Arun K Pujari, Data Mining Techniques, University Press, July 2001

3.W. H. Inmon, Building the Data Warehouse, Wiley Dreamtech India Pvt. Ltd., 4th Edition, Oct.2005.
 

Academic Year: 
2017-18
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