Download Issues Regarding Classification and Prediction

COURSE CURRICULUM
Course Name: DATA MINING & DATA WAREHOUSING
Course Level : Ph.D.
Course Type : PCC
Course Code:
Credit Unit: 04
L
T
P/S
SW/FW
TOTAL CREDIT UNITS
3
1
-
-
4
Course Objective:
The Objective of the course is to:

To explain and apply basic applications, concepts, and techniques of data mining.

To apply classification based on concepts.

They develop skills to solve practical problems in a variety of disciplines.
Pre-requisites:
Basic Knowledge of Database.
Student Learning Outcomes:
After the completion of course, the student will be able to:




Explain about applications, and concepts of data mining.
Describe data preprocessing.
Analyze mining complex types of data.
Explain about classification and prediction.

Course Contents/Syllabus:
Weightage (%)
Module-I Introduction
Fundamentals of data mining, Data Mining Functionalities, Classification
of Data Mining systems, Major issues in Data Mining, Data Warehouse
and OLAP Technology for Data Mining Data Warehouse,
Multidimensional Data Model, Data Warehouse Architecture, Data
Warehouse Implementation, Further Development of Data Cube
Technology, From Data Warehousing to Data Mining.
Module-II Data Preprocessing
Needs Preprocessing the Data, Data Cleaning, Data Integration and
Transformation, Data Reduction, Discretization and Concept Hierarchy
Generation,
Data Mining Primitives, Languages, and System Architectures: Data
Mining Primitives, Data Generalization and Summarization Based
Characterization, Analytical Characterization: Analysis of Attribute
Relevance, Mining Class Comparisons: Discriminating between Different
Classes, Mining Descriptive Statistical Measures in Large Databases.
Association Rules in Large Databases
Association Rule Mining, Mining Single Dimensional Boolean Association
Rules from Transactional Databases, Mining Multilevel Association Rules
from Transaction Databases, Mining Multidimensional Association Rules
from Relational Databases and Data Warehouses, From Association
Mining to Correlation Analysis, Constraint Based Association Mining.
15
25
Module III: Mining
Module IV: Classification and
20
Prediction
Issues Regarding Classification and Prediction, Classification by
Decision Tree Induction, Bayesian Classification, Classification by
Backpropagation, Classification Based on Concepts from Association
Rule Mining, Other Classification Methods, Prediction, Classifier
Accuracy. Types of Data in Cluster Analysis, A Categorization of Major
Clustering Methods, Partitioning Methods, Density Based Methods, Grid
Based Methods, Model Based Clustering Methods, Outlier Analysis.
Module V: Mining Complex Types of Data
20
20
Multidimensional Analysis and Descriptive Mining of Complex, Data
Objects, Mining Spatial Databases, Mining Multimedia Databases,
Mining Time Series and Sequence Data, Mining Text Databases, Mining
the World Wide Web.


Pedagogy for Course Delivery:
The class will be held with the help of lectures. In addition to assigning the case studies, the course
instructor will spend considerable time in understanding the concepts.


Assessment/ Examination Scheme:
Theory L/T (%)
Lab/Practical/Studio (%)
Total
100
100

-
Theory Assessment (L&T):
Continuous Assessment/Internal Assessment
End Term
Examination
Components
(Drop down)
Mid-Term
Exam
Assignments
Project/Viva
Attendance
Weightage (%)
10
10
5
5
70
Text Books/Reference Books:
Text:

Data Mining - Concepts and Techniques, Jiawei Han & Micheline Kamber , Morgan
Kaufmann Publishers, 2006.
References:

Data Mining Introductory and advanced topics “Margaret H Dunham, Pearson education,
2006.
Journals:
 International Journal of Data Mining and Bioinformatics, Inderscience
 Information and Computation, Elsevier