DATA WAREHOUSING AND MINING

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY ANANTAPUR.

MCA II-II Sem                                                                                                        

(9F00403) DATA WAREHOUSING AND MINING

UNIT I

Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or a Data Warehouse System, Major issues in Data Mining.

Data Preprocessing: Need for Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.

UNIT II

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

UNIT III

Data Cube Computation and Data Generalization: Efficient Methods for Data Cube Computation, Further Development of Data Cube and OLAP Technology, Attribute-Oriented Induction.

UNIT IV

Mining Frequent Patterns, Associations and Correlations: Basic Concepts, Efficient and Scalable Frequent Itemset Mining Methods, Mining various kinds of Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining 

UNIT V

Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Rule-Based  Classification, Classification by Backpropagation, Support Vector Machines, Associative Classification, Lazy Learners, Other Classification Methods, Prediction, Accuracy and Error measures, Evaluating the accuracy of a Classifier or a Predictor, Ensemble Methods

UNIT VI

Cluster Analysis Introduction :Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data, Constraint-Based Cluster Analysis, Outlier Analysis

Mining Streams, Time Series and Sequence Data: Mining Data Streams, Mining Time-Series Data, Mining Sequence Patterns in Transactional Databases, Mining Sequence Patterns in Biological Data, Graph Mining, Social Network Analysis and Multirelational Data Mining

UNIT VII

Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web.

UNIT VIII

Applications and Trends in Data Mining:  Data Mining Applications, Data Mining System Products and Research Prototypes, Additional Themes on Data Mining and Social Impacts of Data Mining.

REFERENCES:

1.  Data Mining – Concepts and Techniques  - Jiawei Han & Micheline Kamber, Morgan Kaufmann Publishers, 2^nd Edition, 2006.

2.   Introduction to Data Mining – Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Pearson education.

3. Data Warehousing in the Real World – Sam Aanhory & Dennis Murray Pearson  Edn Asia.

4.Insight into Data Mining,K.P.Soman,S.Diwakar, V.Ajay,PHI,2008.

5.Data Warehousing Fundamentals – Paulraj Ponnaiah Wiley student Edition

6.The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley student edition

7.Building the Data Warehouse By William H Inmon, John Wiley & Sons Inc, 2005.

8.Data Mining Introductory and advanced topics –Margaret H Dunham, Pearson education

9.Data Mining Techniques – Arun  K Pujari,2^nd edition, Universities Press.

10.   Data Mining,V.Pudi and P.Radha Krishna, Oxford University Press.