Data Mining Concepts and Techniques (Syllabus)

      DATA MINING COMCEPTS AND TECHNIQUES

UNIT :1

An idea on Data Warehouse, Data Mining-KDD versus data mining, Stages of the Data Mining Process-Task primitives., Data Mining Techniques - Data mining knowledge representation. 

UNIT II:

Data mining query languages- Integration of Data Mining System with a Data Warehouse Issues, data pre-processing - Data Cleaning, Data transformation - Feature selection Dimensionality reduction.

UNIT III:

Concept Description: Characterization and comparison What is Concept Description, Data Generalization by Attribute-Oriented Induction(AOI), AOI for Data Characterization, Efficient Implementation of AOI.

Mining Frequent Patterns, Associations and Correlations: Basic Concepts, Frequent Item set Mining Methods: Apriori method, generating Association Rules, Improving the Efficiency of Apriori, Pattern-Growth Approach for mining Frequent Item sets.

UNIT IV:

Classification Basic Concepts: Basic Concepts, Decision Tree Induction: Decision Tree Induction Algorithm, Attribute Selection Measures, Tree Pruning. Bayes Classification Methods.

UNITV:

Classification by Back Propagation:  Multi_Layer Feed Forward Neural Network, Support

Vector Machines: Cases when the data are linearly separable and linearly in separable.

Cluster analysis: Cluster Analysis, Partitioning Methods, Hierarchal methods, Density based methods-DBSCAN.