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Sukanta Ghosh, Lovely Professional University Unit 10: Data Mining Tools and Techniques

Unit 10: Data Mining Tools and Techniques Notes

CONTENTS
Objectives
Introduction

10.1 Data Mining Tools
10.2 Data Mining Techniques
10.2.1 Statistics

10.2.2 Nearest Neighbor
10.2.3 Clustering
10.2.4 Decision Trees
10.2.5 Neural Networks
10.2.6 Rule Induction

10.3 Text Mining
10.4 Web Mining
10.5 Summary

10.6 Keywords
10.7 Review Questions
10.8 Further Readings

Objectives

After studying this unit, you will be able to:

Discuss Various Data Mining Tools
Explain Data Mining Techniques such as Decision Tree, Neural Network, etc.

Introduction

Data Mining can be defined as a technique for extracting the “meaning” contained in information
to allow the understanding needed by a user to make a “right” decision. It is Data Mining that
allows a computer to digest the constant stream of data being generated by the computerized
sensors and monitors of the plant, and then extract from that information that has some meaning
content. Data mining tools and techniques can be used for rationalizing the data so as to reduce
the overload that tends to occur and make it simple for the personnel to make a right decision in
textile industry. In this unit, we will discuss various data mining tools and techniques.

10.1 Data Mining Tools

Data mining tools collect data and model the data to represent the reality. The model will
represent and describe the data relationship and pattern. Based on orientation process, data
mining activities divide into three categories which include discovery, predictive modeling and
forensic analysis. Discovery is the process of finding the hidden patterns in a database without

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