8th Semester: Data Mining and Data Warehousing Micro Syllabus:

Course Title: Data Warehousing and Data Mining

Course no: CSC-459                                                                         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.

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

Lesson Plan

Unit	Course content-breakdown	Lecture Hours	Remarks
1	Introduction to Data Mining and Data Warehousing: -Review  of basic concepts of data warehousing and data mining -Definition of data mining -Definition of data warehousing Reasons for their use and benefits: - Why data mining? - Knowledge discovery process - Benefits of data mining and data warehousing -Application of data mining -Problem and challenges of data mining	5 hrs.
2	Data Warehouse Logical Design: -From Tables and Spreadsheets to Data Cubes -Star Schemas - Fact Constellations - Dimensions - Snowflakes - Materialized View	5 hrs.
3	Data Warehouse Physical Design: - Hardware and I/O Consideration -Parallelism -Indexes	5 hrs.
4	Data Warehousing technologies and implementation: -Data extraction -Transportation -Transformation -Loading and refreshing -Data warehouse support in SQL Server 2008/Oracle 11g	10 hrs.

5	Data Warehouse to Data Mining: -OLAP Architecture -Design and Query Processing -SQL extension for OLAP -Data Mining Tools (WEKA/SQL SERVER/ORACLE/EXCEL/SPSS)	8 hrs.
6	Data Mining Approaches and Methods: - Concept description - Classification - Association rules -Clustering	6 hrs.
7	Mining complex types of data: -Spatial Data mining - Multimedia Data mining -Text mining - Mining world wide web(WWW)	4 hrs.
8	Research trends in data warehousing and data mining: -Data Mining Systems Products and Research Prototypes Theoretical foundations of data mining -Statistical data mining -Visual and audio data mining -Data mining and Collaborative Filtering -Social impact of data mining -Trends in data mining	5 hrs.

Laboratory Work:

1. Installing data mining

2.       Preparing data file for data mining

a.       Creating ARFF File

b.      Converting EXCEL file to ARFF

c.       Editing ARFF data file and change attribute

3.       Data Classification through data mining tools

4.       Data visualization through data mining tools

5.       Factor analysis through data mining tools

6.       Association rule mining though data mining tools

7.       Clustering through data mining tools

8.       Estimation through data mining tools

9.       Warehousing installation and management

10.   Data cleansing

Text Books:                Data Mining Concepts and Techniques, Morgan Kaufmann J. Han, M Kamber  Second Edition ISBN : 978-1-55860-901-3

Data Mining with Microsoft SQL Server 2000 C. Seidman

Homework

Assignment:               Assignment should be given throughout the semester.

Computer Usage:      No specific

Prerequisite:              C, Data Structure, Database

Category Content:    Science Aspect:           40%

                                    Design Aspect:            60%

Name	Campus	Signature
Dr. Subarna Shakya (Expert)	Asst. Dean, Institute of Engineering, Pulchowk
Dilli Prasad Sharma	Amrit Science Campus
Bishnu Gautam	New Summit College
Suresh Gautam	Patan M. Campus
Rajan Karmacharya	St. Xavier College
Karna Dev Bhatta	Siddnath Bigyan Campus
Kumar Poudyal	New Summit College

Feb 9-10, 2012 ( Magh 26^th and 27^th , 2068)

Central Department of Computer Science and Information Technology, TU

Full marks:      60  Pass marks:   24  Time:   3 hours.

Model Question

Bachelor Level/ Fourth Year/Eight Semester/Science

Data Warehousing and Data Mining (CSC-459)

Candidates are required to give their answers in their own words as far as practicable. The figures in the margin indicate full marks.

Group-A

Long Answer Questions (Attempt any Two questions)                                         [2x10=20]

1.      What do you mean by data warehouse? " The Bhatbhateni Super Store" one of the largest departmental stores in the valley. Assume your project team got the opportunity to build the warehouse system of this departmental store. Develop the process architecture of the warehouse system and explain how you manage the responsibilities to build the ware.

2.         What kind of data preprocessing do we need before applying data mining algorithm to any dataset. Explain binning method to handle noisy data with example.

3.      What is association rule analysis? Write APriori Algorithm with its pruning principle.

Group- B

Short Answer  Questions( Attempt any Eight questions)                                  [8x5=40]

4.      What is data mining? Explain the applications of data mining.                                         [5]

5.      What is KDD? Explain the preprocessing steps of KDD with example.                           [5]

6.      Explain OLAP operations with example?                                                                         [5]

7.      List the drawbacks of ID3 algorithm with over-fitting and its remedy techniques            [5]

8.      Write the algorithm for K-means clustering. Compare it with k-nearest neighbor algorithm. [5]

9.      What is text mining? Explain the text indexing techniques.                                              [5]

10.  Describe genetic algorithm using as problem solving technique in data mining.               [5]

11. What do you mean by WWW mining? Explain WWW mining techniques.                  [5]

12.  What is DMQL? How do you define Star Schema using DMQL?                                                [5]

13.  What is Prediction? Explain linear regression with example                                          [5]

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