Page 155 - DCAP606_BUSINESS_INTELLIGENCE

Business Intelligence Sartaj Singh, Lovely Professional University

Notes Unit 11: Data Mining

CONTENTS
Objectives
Introduction

11.1 Data Mining
11.1.1 Process of Knowledge Discovery
11.1.2 Types of Data Mining Tasks

11.1.3 Purpose of Data Mining
11.2 Data Mining Approaches
11.3 Data Mining Uses
11.4 Data Mining Issues
11.5 Data Mining Applications

11.6 Limitations of Data Mining
11.7 Data Mining Models
11.8 Data Mining Algorithms

11.9 Summary
11.10 Keywords
11.11 Review Questions
11.12 Further Readings

Objectives

After studying this unit, you will be able to:

Define data mining
State data mining approaches
Identify data mining uses

Discuss data mining issues
Demonstrate applications of data mining
Explain limitations of data mining
Recognize data mining models

Discuss basics of data mining algorithms
Introduction

Data mining refers to the extraction of hidden predictive information from large databases.
Data mining techniques can yield the benefits of automation on existing software and hardware
platforms. Data mining tools can answer business questions that traditionally were too time

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