Data Mining

 

Unit 1

DATA MINING Introduction – Data – Types of Data – Data Mining Functionalities –Classification of Data Mining Systems – Issues –Data Preprocessing

ASSOCIATION RULE MINING Mining Frequent Patterns   –   Apriori Algorithm Description, -

Unit 2

ASSOCIATION RULE MINING – Two Illustrative Examples- Mining various Kinds of Association Rules, Correlation Analysis – Constraint Based Association Mining.

CLASSIFICATION AND PREDICTION Basic Concepts - Decision Tree Induction - Bayesian Classification – Rule Based Classification

Unit 3

CLASSIFICATION AND PREDICTION– Classification by Back propagation, Basic Concepts - Decision Tree Induction - Bayesian Classification, Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction. C4.5 Algorithm Description- C4.5 Features - Two Illustrative Examples.

Unit 4

CLUSTERING AND TRENDS IN DATA MINING Cluster Analysis - Types of Data – Partitioning Methods: K-means, K-mediods, PAM, CLARA, CLARANS – Hierarchical Methods: AGNES, DIANA, Density-Based Methods: DBSCAN –Grid Based Methods – STING, CLIQUE, Data Mining Applications.

Unit 5

MINING Mining the World Wide Web - Page Rank Algorithm, Text mining, Mining Time Series Data, The CART Algorithm Briefly Stated, Ensemble methods-Increasing the Accuracy, Mining genomic data.

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