CMS-A-DSE-A--2-TH: Data Mining and its Applications DSE-A: Choice-2: Theory, Credit:04, Contact hours: 60.

 Introduction

Definition of Data Mining, Data pre-processing, Data cleaning, Data transformation,

Data Reduction, Data Visualization, Data extraction from large dataset, Data integration,

sub-sampling, Feature selection, Scalability issues of data mining algorithms, text

mining, web mining.

15hours

Classification and Prediction

Structural patterns of data, Tools for pattern recognition (preliminary concept), Linear

models for classification, Evaluating the accuracy of the classifier or predictor, Bayesian

Classification, Training and Test sets, Parametric and Non-parametric Learning,

Minimum Distance Classifiers, k-NN rule, Discriminant Analysis, Decision trees.

Similarity Measure, Basic hierarchical and non-hierarchical Clustering algorithms,

Some Applications, Neural Learning.

30hours

Data Warehousing (DWH)

Introduction: Definition and description, need for data ware housing, need for strategic

information, failures of past decision support systems, Application of DWH.