JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To understand data mining concepts.
• To learn about various data preprocessing techniques.
• To learn about data warehousing.
• To learn about various data mining functionalities such as association rule mining, clustering, classification and outlier analysis.

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, 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, Usage of Data Warehousing Online Analytical Processing and Mining Data Cube Computation: Efficient Methods for simple Data Cube Computation (Full Cube, Iceberg Cube, Closed Cube and Shell Cube), Discovery Driven exploration of data cubes, Attribute-Oriented Induction for data characterization and its implementation

UNIT - III

Mining Frequent Patterns, Associations and Correlations: Basic Concepts, The Apriori algorithm for finding frequent itemsets using candidate generation, Generating association rules from frequent itemsets, Mining frequent itemsets without candidate generation, Mining various kinds of Association Rules, Correlation Analysis

UNIT - IV

Classification and Prediction: Description and comparison of classification and prediction, preparing data for Classification and Prediction Classification by Decision Tree Induction, Bayesian Classification, Rule-Based Classification, Classification by Backpropagation Prediction, linear and non-linear regression, evaluating accuracy of a Classifier or a Predictor

UNIT - V

Cluster Analysis: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, k-means and k-mediods methods, CLARANS, Agglomerative and divisive hierarchical clustering, chameleon dynamic modeling, DBSCAN, Grid based clustering method: STING, Conceptual Clustering, Constraint-Based Cluster Analysis, Outlier Analysis.

Text Books:

• 1. Data Mining – Concepts and Techniques - Jiawei Han, Micheline Kamber and Jian Pei, 3rd edition, Morgan Kaufmann Publishers, ELSEVIER.
• 2. Introduction to Data Mining – Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Pearson education. Page 16 of 55 R19 M.TECH. CSE/CS

References:

• 1. Data Warehousing in the Real World – Sam Aanhory & Dennis Murray Pearson Edn Asia.
• 2. Insight into Data Mining, K. P. Soman, S. Diwakar, V. Ajay, PHI, 2008.
• 3. Data Warehousing Fundamentals – Paulraj Ponnaiah Wiley student Edition
• 4. The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley student edition
• 5. Building the Data Warehouse By William H Inmon, John Wiley & Sons Inc, 2005.
• 6. Data Mining Introductory and advanced topics –Margaret H Dunham, Pearson education
• 7. Data Mining Techniques – Arun K Pujari, 2nd edition, Universities Press.
• 8. Data Mining, V. Pudi and P. Radha Krishna, Oxford University Press.
• 9. Data Mining: Methods and Techniques, A.B. M Shawkat Ali and S. A. Wasimi, Cengage Learning.
• 10. Data Warehouse 2.0, The Architecture for the next generation of Data Warehousing, W.H. Inmon, D. Strauss, G. Neushloss, Elsevier, Distributed by SPD. Page 17 of 55 R19 M.TECH. CSE/CS M. Tech - CSE/CS – I Year – I Semester Common to CSE and CS Syllabus