Download CS6T4 - DATA WAREHOUSING AND MINING

Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya
Department of Computer Science and Engineering
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CS6T4 - DATA WAREHOUSING AND MINING
(For Students admitted from 2014 onwards)
PREREQUISITES:
Knowledge of data base management systems.
AIM:
To gain basic knowledge on warehousing and handling of large volume of data.
OBJECTIVES:
This course will help students to achieve the following objectives:
1. Design of data warehouse
2. Methods to interpret knowledge from data warehouse.
OUTCOMES:
At the end of this course students will be able to:
1. Develop the data warehouse with suitable schema.
2. Create simple data mining applications using various functionalities of data mining.
UNIT- I
DATA WAREHOUSE
Evolution of Data base Technology - Definition: Data Warehouse - Differences between
Operational Data base systems and Data Warehouses - Multidimensional Data Model - OLAP
Operations - Warehouse Schema - Data Warehousing Architecture - Warehouse Server Metadata - OLAP engine - The tasks in Building a Data Warehouse - Data warehouse backend
Process - Data warehouse applications
UNIT- II
INTRODUCTION TO DATA MINING & PREPROCESSING
Data mining: Definition - Knowledge discovery in database (KDD) vs. Data mining - DBMS vs
DM– Stages of the Data Mining Process-task primitives, Data Mining Techniques -Data mining
knowledge representation – Data mining query languages, Integration of a Data Mining System
with a Data Warehouse – Issues, Data preprocessing – Data cleaning, Data transformation,
Feature selection, Dimensionality reduction, Discretization and generating concept hierarchies.
UNIT - III
ASSOCIATION & CLUSTERING
Mining frequent patterns- Market Basket Analysis –Frequent Itemset Mining Methods, Pattern
Evaluation Methods, Advanced Pattern Mining - Multilevel, Multidimensional space, Constraintbased Pattern Mining, Mining High Dimensional Data and Colossal Patterns.
Cluster Analysis, Partitioning Methods, Hierarchical Methods, Density-Based and Grid-Based
Methods, Evaluation of Clustering
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Syllabus B.E[CSE] Full Time
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya
Department of Computer Science and Engineering
UNIT- IV
CLASSIFICATION
Decision Tree Induction - Bayesian Classification – Rule Based Classification – Model
Evaluation, Enhancing Model Accuracy, Classification: Advanced Methods – Bayesian Belief
Networks, Classification by Back propagation – Support Vector Machines – Associative
Classification – Lazy Learners , Genetic Algorithms, Rough Set Approaches, Fuzzy Set
Approaches
UNIT - V
MINING COMPLEX TYPES, DATA MINING APPLICATIONS AND CASE STUDIES
Introduction to Mining Data Streams – Mining Time-Series Data – Graph Mining – Social
Network Analysis.Data warehousing and mining Applications - Products - Case studies - The
Future of Data Mining - Privacy and Security of Data Mining
TEXT BOOK
1. Data Mining: Concepts and Techniques: Concepts and Techniques
Micheline Kamber, Jian Pe , 3rd Edition, Elsevier, 2011
By Jiawei Han,
REFERENCE BOOKS
1. Arun K Pujari ," Data mining" , Third Edition, Universities Press (India) Private Limited,
2013
2. C.S.R. Prabhu , "Data Ware housing: Concepts, Techniques, Products and Applications",
Third Edition , Prentice Hall of India, 2008.
3. Morgrat A. Dunham, " Data Mining: Introductory And Advanced Topics", Third Edition ,
Pearson Education, 2008.
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Syllabus B.E[CSE] Full Time