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Title of the Subject: Data Warehousing and Data Mining Course Code: MCA 209

Teaching Scheme: Lectures: 4 Hrs/Week Practical : 2 Hrs/Week

Examination Scheme: Theory Paper: 100 Marks (3 Hrs) Practical Exam: 25 Marks Term Work: 25 Marks


  • -

    To familiarize with the fundamental concepts of Data warehousing and OLAP

  • -

    To develop the concepts of data mining methods in database management skills

  • -

    To be able to efficiently design and manage data storages using data warehousing, OLAP,

and data mining techniques,

  • -

    To use the concepts in Text mining, web mining and Knowledge Discovery

Unit 1- Introduction to Data Warehousing:

(8 hrs )

Introduction to Decision Support System: DSS Defined, History of DSS, Ingredients of DSS, Data and Model Management, DSS Knowledge base, User Interfaces, The DSS Users, Categories and Classes of DSSs Need for data warehousing, Operational & informational data, Data Warehouse

definition and characteristics, Operational Data Stores.

Unit 2- Data warehouse Components

(8 hrs )

Architectural components, Data Preprocessing: Why Preprocess Data? Data Cleaning Techniques, Data Integration and Transformation, Data Reduction Techniques, Discretization and Concept Hierarchy Generation for numeric and categorical data, Significant role of metadata , Building a

Data warehouse, Benefits of Data Warehousing.

Unit 3- OLAP in the Data Warehouse

(8 hrs )

A Multidimensional Data Model, Schemas for Multidimensional Databases: Stars, Snowflakes, Star join and Fact Constellations Measures, Concept Hierarchies, OLAP Operations in the Multidimensional Data Model, Need for OLAP, OLAP tools , Mining Multimedia Databases,

Mining Text Databases, Mining the World Wide Web.

Unit 4- Data Mining Algorithms

(8 hrs )

Concept Description: What is Concept Description? Data Generalization and Summarization-Based Characterization, Mining Descriptive Statistical Measures in Large Databases. Mining Association Rules: Association Rule Mining, Market Basket Analysis, Association Rule classification, The Apriori Algorithm, Mining Multilevel Association Rules, Constraint-Based Association Mining, Sequential mining. Classification and Prediction: What is Classification and Prediction? Data Classification Process, Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian


Unit 5- Classification, Knowledge Discovery

(8 hrs )

Classification Based on Association Rule Mining, Other Classification Methods Cluster Analysis: What is Cluster Analysis? Types of Data in Cluster Analysis, A Categorization of Clustering Methods. Introduction to Knowledge Discovery, innovative techniques for knowledge discovery, application of those techniques to practical tasks in areas such as fraud detection, scientific data analysis, and web mining, Introduction to huge data sets such as Web, telecommunications networks, relational databases, object-oriented databases, and other sources of structured and semi-structured data, Problem of Large Data sets

Text/Reference Books –

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