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Management Support Systems Anuj Sharma, Lovely Professional University

Notes Unit 9: Data Mining

CONTENTS
Objectives
Introduction

9.1 Concepts of Data Mining
9.1.1 Types of Information
9.1.2 Data Mining and Knowledge Discovery

9.1.3 Types of Data
9.1.4 Data Mining Functionalities
9.1.5 Working of Data Mining
9.1.6 Categorization of Data Mining Systems
9.1.7 Issues in Data Mining

9.2 Applications of Data Mining
9.3 Summary
9.4 Keywords

9.5 Review Questions
9.6 Further Readings

Objectives

After studying this unit, you will be able to:
Discuss the Concepts of Data Mining

Explain Data Mining and Knowledge Discovery
Discuss Applications of Data Mining

Introduction

Data mining, the extraction of hidden predictive information from large databases, is a powerful
new technology with great potential to help companies focus on the most important information
in their data warehouses. Data mining tools predict future trends and behaviors, allowing
businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses
offered by data mining move beyond the analyses of past events provided by retrospective
tools typical of decision support systems. Most companies already collect and refine massive
quantities of data.

9.1 Concepts of Data Mining

Data mining uses a relatively large amount of computing power operating on a large set of data
to determine regularities and connections between data points. Algorithms that employ techniques
from statistics, machine learning and pattern recognition are used to search large databases
automatically. Data mining is also known as Knowledge-Discovery in Databases (KDD).

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