Data Warehousing and Data Mining |
UNIT I |
Introduction: Data Mining tasks – Data Mining versus Knowledge
Discovery in Data bases – Relational databases – Data warehouses –
Transactional databases – Object oriented databases – Spatial databases –
Temporal databases – Text and Multimedia databases – Heterogeneous
databases - Mining Issues – Metrics – Social implications of Data mining. |
UNIT II |
Data Preprocessing: Why Preprocess the data – Data cleaning – Data
Integration – Data Transformation – Data Reduction – Data Discretization. |
UNIT III |
Data Mining Techniques: Association Rule Mining – The Apriori
Algorithm – Multilevel Association Rules – Multidimensional Association
Rules – Constraint Based Association Mining. |
UNIT IV |
Classification and Prediction: Issues regarding Classification and
Prediction – Decision Tree induction – Bayesian Classification – Back
Propagation – Classification Methods – Prediction – Classifiers accuracy. |
UNIT V |
Clustering Techniques: cluster Analysis – Clustering Methods –
Hierarchical Methods – Density Based Methods – Outlier Analysis –
Introduction to Advanced Topics: Web Mining , Spatial Mining and Temporal
Mining. |
Text Books |
(i) J. Han and M. Kamber , 2001, Data Mining: Concepts and Techniques, Morgan
Kaufmann, .New Delhi. |
Reference Books |
(i) M. H.Dunham, 2003, Data Mining : Introductory and Advanced Topics , Pearson
Education, Delhi.
(ii) Paulraj Ponnaiah, 2001, Data Warehousing Fundamentals, Wiley Publishers.
(iii) S.N. Sivananda and S. Sumathi, 2006, Data Mining, Thomsan Learning, Chennai. |
Website, E-learning resources |
(i) http://www. academicpress.com
(ii) http://www.mkp.com |