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P 5.4 DATA MINING SOCIETY AND TRENDS

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P 5.3 DATA MINING APPLICATIONS

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P 5.2 OTHER METHODOLOGIES OF DATA MINING

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P 5.1 MINING COMPLEX DATA TYPE

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P 4.5 CLUSTER ANALYSIS GRID BASED METHOD

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P 4.4 CLUSTER ANALYSIS DENSITY BASED METHODS

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P 4.3 CLUSTER ANALYSIS HIERARCHICAL METHOD

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P 4.2 CLUSTER ANALYSIS PARTITIONING METHOD

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P 4.1 CLUSTER ANALYSIS BASIC CONCEPTS

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3.5 DATA MINING SUPPORT VECTOR MACHINES

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3.4 DATA MINING Classification by Backpropagation

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3.3 DATA MINING Naive Bayes Classifier with an example

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

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3.1 DATA MINING - Classification_ Basic Concepts

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2.5 DATA MINING Pattern Evaluation Methods

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2.4.1 DATA MINING FREQUENT PATERN MINING Using Vertical Data Format

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2.4 DATA MINING Frequent Pattern Mining

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2.3 DATA MINING FP Growth

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2.2 DATA MINING FREQUENT ITEMSETS - Improving Apriori Algorithm

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2.1 DATA MINING FREQUENT ITEMSETS

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1.7 Measuring Data Similarity and Dissimilarity

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1.6 Getting to Know Your Data Data Visualization

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1.5 Getting to Know Your Data Basic Statistical Descriptions of Data

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1.4 Getting to know your Data Data Objects and Attribute Types

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

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1.2 Data Mining Descriptive and Predictive Data Mining

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1.1 Data Mining Concepts and Techniques.

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