UNIT-I
Introduction: Why Data Mining? What is Data Mining? What kinds of data can be mined? What kinds of
patterns can be mined? Which technologies are used? Which kinds of applications are Targeted? Major
issues in Data Mining. Getting to know your data: Data objects and attributed types. Basic statistical
descriptions of data. Data visualization, Measuring data similarity and dissimilarity.
UNIT-II
Mining frequent patterns, Associations and correlations: Basic concepts and methods, Frequent Item set
Mining Methods, which patterns are interesting? Pattern evaluation methods.
UNIT-III
Classification: Basic concepts, Decision tree induction, Bayes classification methods, Advance methods,
Bayesian Belief Network, Classification by back propagation, Support vector machine.
UNIT-IV
Cluster Analysis: Concepts and Methods: Cluster Analysis, Partitioning Methods, Hierarchical Methods,
Density-Based Methods, Grid-Based Methods, Evaluation of clustering.
UNIT-V
Data Mining Trends and Research Frontiers: Mining Complex Data Types, Other Methodologies of Data
Mining, Data Mining Applications, Data Mining and Society, Data Mining trends.
Suggested Readings:
1. Jiawei Han, Micheline Kamber, Jin Pei, Data Mining: Concepts & Techniques, 3rd Edition., Morgon
Koffman ,2011
2. Vikram Pudi, P. Radha Krishna, Data Mining, Oxford University Press, 1st Edition, 2009.
3. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Education,
2008.
