Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4

1.   Introduction to Data Mining:
Motivation     and    importance,  What  is   Data  Mining,  Relational  Databases,  Data Warehouses,  Transactional  Databases,  Advanced  Database  Systems  and  Advanced Database  Applications,   Data  Mining  Functionalities,  Interestingness  of  a  pattern Classification of Data Mining Systems, Major issues in Data Mining.
2.   Data Warehouse and OLAP Technology for Data Mining
What  is  a   Data  Warehouse?    Multi-Dimensional  Data  Model,    Data   Warehouse Architecture, Data Warehouse Implementation, Development of Data Cube Technology, Data Warehousing to Data Mining
3    Data Preprocessing
Why Pre-process the Data?  Data Cleaning,  Data Integration and Transformation
Data Reduction,  Discretization and Concept Hierarchy Generation
4    Data Mining Primitives, Languages and system Architectures,Data Mining Primitives:What defines a Data Mining Task?, A Data Mining query language, Designing Graphical Use Interfaces Based on a Data Mining Query language,Architectures of Data Mining Systems
5    Concept Description: Characterization and comparison ,What is Concept Description?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
6    Mining Association rule 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
7    Classification and prediction,Concepts and Issues regarding  Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Classification by  Back-propagation, Classification Based on Concepts from Association Rule Mining,  Other Classification Methods like k-Nearest Neighbor Classifiers, Case- Based Reasoning, Generic Algorithms, Rough Set Approach, Fuzzy Set Approaches, Prediction,  Classifier Accuracy
8    Cluster Analysis
What is Cluster Analysis?  Types of Data in Cluster Analysis,  A Categorization of Major
Clustering Methods

 

Text Book: 
Data Mining Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan
Kaufman Publications
Reference Books:
1. Introduction to Data Mining,  Adriaan,   Addison Wesley Publication
2. Data Mining Techniques,    A.K.Pujari,   University Press

tejus mahiCSE 4.2 SyllabusIT 4.2 SyllabusCSE,CSE Syllabus,Data Ware Housing And Data Mining Syllabus,IT,IT Syllabus
Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4 1.   Introduction to Data Mining: Motivation     and    importance,  What  is   Data  Mining,  Relational  Databases,  Data Warehouses,  Transactional...