Sankar V

P 5.4 DATA MINING SOCIETY AND TRENDS

P 5.3 DATA MINING APPLICATIONS

P 5.2 OTHER METHODOLOGIES OF DATA MINING

P 5.1 MINING COMPLEX DATA TYPE

P 4.5 CLUSTER ANALYSIS GRID BASED METHOD

P 4.4 CLUSTER ANALYSIS DENSITY BASED METHODS

P 4.3 CLUSTER ANALYSIS HIERARCHICAL METHOD

P 4.2 CLUSTER ANALYSIS PARTITIONING METHOD

P 4.1 CLUSTER ANALYSIS BASIC CONCEPTS

3.5 DATA MINING SUPPORT VECTOR MACHINES

3.4 DATA MINING: Classification by Backpropagation

3.3 DATA MINING Naive Bayes Classifier with an example

3.2 DATA MINING : Enhancements to Basic Decision Tree Induction & Introduction to Naïve Bayesian

3.1 DATA MINING - Classification_ Basic Concepts

2.5 DATA MINING Pattern Evaluation Methods

2.4.1 DATA MINING FREQUENT PATERN MINING: Using Vertical Data Format

2.4 DATA MINING Frequent Pattern Mining

2.3 DATA MINING FP Growth

2.2 DATA MINING FREQUENT ITEMSETS - Improving Apriori Algorithm

2.1 DATA MINING FREQUENT ITEMSETS

1.7 Measuring Data Similarity and Dissimilarity

1.6 Getting to Know Your Data: Data Visualization

1.5 Getting to Know Your Data: Basic Statistical Descriptions of Data

1.4 Getting to know your Data : Data Objects and Attribute Types

1.3 Supervised and Unsupervised Learning Techniques and Major issues in Data Mining

1.2 Data Mining: Descriptive and Predictive Data Mining

1.1 Data Mining: Concepts and Techniques.