Data Mining

Table of Contents

Abstract3

Background4

Introduction5

Discussion6

Data Mining Tools7

Types of Data Mining Tools7

Data Mining Techniques and Their Application8

Data Mining in Business9

Science and Engineering11

Conclusion14

References15

Bibliography16

Abstract

This research is conducted to explore the tools, techniques and effect of data mining in an organization. It is very important for business developers to have a clear understanding of the role of data mining in reducing the cost and improving the sales rate of new services and products. They must beware of all the techniques and tools that can play significant role in enhancing their business. It can also help them to be aware of business fraud.

Background

For centuries, manual extraction of patterns from data has been observed. Regression analysis (1800s) and Bayes' theorem (1700s) are considered as early methods of recognizing patterns in data. The increasing power, ubiquity and proliferation of computer technology have result in a dramatic increase of data storage, collection, and manipulation ability. With the growth in complexity and size of data, comparison between direct "hands-on" data analysis and indirect, automated data processing is observed, assisted by several other discoveries in computer science including genetic algorithms (1950s), neural networks, decision trees (1960s), cluster analysis, and support vector machines (1990s). The process of implementing these methods for revealing the hidden patterns in large data sets is termed as data mining. It bridges the gap from artificial intelligence and applied statistics to database management by exploiting the way in which data is indexed and stored in databases in order to execute the actual discovery and learning algorithms in a more effective manner, by simplifying certain methods to be implemented to ever larger data sets (Dong, and Pei, 2007).

Introduction

In last few years, human capabilities of collecting and generating data have rapidly enhanced and this is all as a result of implementation of advance technologies. The factors that are responsible for this include the digitalization and computerization of scientific, business and government transactions, the widespread usage of barcode scanners, digital camera and publication tools mostly for commercial products, and technological advancement in tools for data collection that range from image platforms and scanned text to satellite remote sensing systems. Moreover, a tremendous amount of data and information has flooded due to the high usage of World Wide Web as an information system globally. This tremendous growth in transient and stored data has give rise to the requirement of automated tools and new techniques for transforming huge amounts of data intelligently into useful knowledge and information. The techniques and concepts of data mining are flourishing and promising frontier in data and information systems as well as in their applications. The term data mining is also known as knowledge discovery from data (KDD), which is a convenient and automated extraction of patters that represent data warehouses, catchable in large databases, knowledge implicitly stored, the Web and other data streams or massive information repositories. Data mining has drawn from different areas involving machine learning, database technology, neural networks, information retrieval, artificial intelligence, knowledge-based systems, data visualization and high-performance computing. Several effective techniques have ...