Tribhuvan University
Institute of Science and Technology
Bachelor of Science in Computer Science and Information
Technology
Course Title: Data Warehousing and Data Mining
Course no.: CSC-451 Full
Marks: 60+20+20
Credit Hours: 3 Pass
Marks: 24+8+8
Nature of Course: Theory
(3 Hrs.) + Lab (3 Hrs.)
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.
Course Contents:
Unit 1: (5
Hrs.)
Concepts of Data Warehouse and
Data Mining including its functionalities, 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
Unit 2: (4
Hrs.)
DBMS vs. Data Warehouse, Data
marts, Metadata, Multidimensional data model, Data Cubes, Schemes for
Multidimensional Database: Stars, Snowflakes and Fact Constellations.
Unit 3: (6
Hrs.)
Data Warehouse Architecture,
Distributed and Virtual Data Warehouse, Data Warehouse Manager, OLTP, OAP,
MOLAP, HOLAP, types of OLAP, Servers.
Unit 4: (4
Hrs.)
Computation of Data Cubes,
modeling: OLAP data, OLAP queries, Data Warehouse back end tools, tuning and
testing of Data Warehouse.
Unit 5: (4
Hrs.)
Data Mining definition and Task,
KDD versus Data Mining, Data Mining techniques, tools and application.
Unit 6: (5
Hrs.)
Data mining query languages, data
specification, specifying knowledge, hierarchy specification, pattern
presentation & visualization specification, data languages and
standardization of data mining.
Unit 7: (6
Hrs.)
Mining Association Rules in Large
Database: Association Rule Mining, why Association Mining is necessary, Pros
and Cons of Association Rules, Apriori Algorithm.
Unit 8: (7
Hrs.)
Classification and Prediction:
Issues Regarding Classification and Prediction, Classification by Decision Tree
Induction, Introduction to Regression, Types of Regression, Introduction to
Clustering, K-mean and K-Mediod Algorithms.
Unit 9: (4
Hrs.)
Mining complex Types of Data:
Mining Text Databases, Mining the World Wide Web, Mining Multimedia and Spatial
Databases.
Laboratory Works: Cover all the concept of data warehouse and
mining mentioned in a course.
Samples
- Creating a simple data warehouse
- OLAP operations: Roll Up, Drill Down, Slice, Dice through SQL Server
- Concepts of data cleaning and preparing for operation
- Association rule mining through data mining tools
- Data Classification through data mining tools
- Clustering through data mining tools
- Data visualization through data mining tools
- Data Mining Concepts and Techniques, Morgan Kaufmann J. Han, M. Kamber Second Edition ISBN: 987-1-55860-901-3
- Data Warehousing in the Real Worlds – Sam Anahory and Dennis Murray, Pearson Edition Asia.
- Data Mining Techniques – Arun K. Pajari, University Press.
- Data Mining – Pieter Adriaans, DolfZantinge.
- Data Mining, Alex Berson; Stephen Smith, KorthTheorling, TMH.
- Data Mining, Adriaans, Addison – Wesley Longman.
An interesting part of the data mining phenomenon is its being anchored on association rules.This paradoxical relationship between things is shown in the “if/then statements.” The “if” statement is called the antecedent while the “then” statement is the consequence.An association rule is made up of two parts namely: an antecedent (if); and a consequent (then). The antecedent is an item that is found in the information bank. In addition, the consequent is another item which is found to be combined with the antecedent.Association rules are important in data mining particularly in analyzing and predicting consumer behavior.. More Helpful..
ReplyDeleteThe Role of Association Rules in Data Mining
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