Data Warehousing and Data Mining Syllabus

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This page contains Syllabus of Data Warehousing and Data Mining of CSIT.

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Data Warehousing and Data Mining

Course

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Title Data Warehousing and Data Mining
Short Name
Course code CSC451
Nature of course Theory + Lab
Semester eighth-semester
Full marks 60 + 20 + 20
Pass marks 24 + 8 + 8
Credit Hrs 3
Elective/Compulsary Compulsary

Course Description

Course Synopsis: Analysis of advanced aspect of data warehousing and data mining.

Goal: This course introduces advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the current technologies


Units and Unit Content

1. Introduction
teaching hours: 5 hrs

What motivated Data mining? What is Data Mining?

  • Types of databases (Relational database, Data Warehouses,  Transactional Database)
  • Functionalities of data mining – What kinds of Pattern can be mined?
  • Association Analysis, Cluster Analysis, Outlier Analysis, Evolution Analysis
  • Stages of Knowledge discovery in database(KDD)
  • Setting up a KDD environment
  • Issues in Data Warehouse and Data Mining
  • Application of Data Warehouse and Data Mining

2. Data Warehouse for Data mining
teaching hours: 4 hrs
  • Differences between operational database systems and data warehouses
  • Data Warehouse Architecture
  • Distributed and Virtual Data Warehouse
  • Data Warehouse Manager
  • Data marts, Metadata, Multidimensional data model
  • From Tables and Spread Sheets to Data Cubes
  • Star schema, Snowflake schema and Fact constellation schema

3. OLAP technology for Data Mining
teaching hours: 6 hrs
  • On-line analytical processing models and operations (drill down, drill up, slice, dice, pivot)
  • Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP
  • OLTP


4. Tuning for data warehouse
teaching hours: 4 hrs
  • Computation of Data Cubes, modeling
  • OLAP data, OLAP queries
  • Data Warehouse back end tools
  • Tuning and testing of Data Warehouse of Data Warehouse.

5. Data Mining techniques
teaching hours: 4 hrs
  • Data Mining definition and Task
  • KDD versus Data Mining
  • Data Mining techniques, tools and application
6. Data mining query languages
teaching hours: 5 hrs
  • Data mining query languages
  • Data specification, specifying knowledge, hierarchy specification, pattern presentation & visualization specification
  • Data mining languages and standardization of data mining

7. Association analysis
teaching hours: 6 hrs
  •  Association Rule Mining
  • why Association Mining is necessary?
  • Pros and Cons of Association Rules
  • Apriori Algorithm

8. Cluster analysis, Classification and Predication
teaching hours: 7 hrs
  • What is classification? What is predication?
  • Issues regarding classification and prediction (Preparing the data for classification and prediction, Comparing classification methods)
  • Classification by decision tree induction (Extracting classification rules from decision trees)
  • Bayesian Classification
  • Classification by back propagation
  • Introduction to Regression (Types of Regression)
  • Clustering Algorithm (K-mean and K-Mediod Algorithms)


9. Advanced concepts in data mining
teaching hours: 4 hrs
  • Mining Text Databases
  • Mining the World Wide Web
  • Mining Multimedia and Spatial Databases

Lab and Practical works

Laboratory Works:

Cover all the concept of datawarehouse and mining mention in a course


Samples

1. Creating a simple data warehouse

2. OLAP operations: Roll Up, Drill Down, Slice, Dice through SQL- Server

3. Concepts of data cleaning and preparing for operation

4. Association rule mining though data mining tools

5. Data Classification through data mining tools

6. Clustering through data mining tools

7. Data visualization through data mining tools