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7 - Indira Gandhi Centre for Atomic Research

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Knowledge Discovery Tools and Techniques<br />

Soundararajan E,.Joseph J.V.M, Jayakumar C. and Somasekharan M.<br />

Abstract<br />

As digital in<strong>for</strong>mation is growing in organizations, massive amount of data are<br />

generated which exist in database systems. This explosive growth of in<strong>for</strong>mation<br />

requires new analysis techniques that can intelligently trans<strong>for</strong>m the useful data into<br />

knowledge. Knowledge discovery is defined as the non-trivial process of identifying<br />

valid, novel, potentially useful and ultimately understandable patterns in data. It uses<br />

machine learning, statistical techniques and visualization techniques to discover and<br />

present knowledge in a <strong>for</strong>m that is easily comprehensible. Knowledge discovery<br />

takes the raw results from data mining and carefully and accurately trans<strong>for</strong>ms them<br />

into useful and understandable in<strong>for</strong>mation. This paper gives a technical overview of<br />

knowledge discovery tools, and techniques. It emphasizes the need <strong>for</strong> a knowledge<br />

management system <strong>for</strong> digital library. Finally the suggestions <strong>for</strong> improving the<br />

per<strong>for</strong>mance of knowledge discovery tools are highlighted.<br />

Keywords: Data Mining, Pattern, Knowledge discovery, Knowledge Discovery<br />

Tools, Digital Library.<br />

1. Introduction<br />

Knowledge management is a systematic process of acquiring, organizing, sustaining,<br />

applying, sharing and renewing both tacit and explicit knowledge to enhance the<br />

organizational per<strong>for</strong>mance, increase organizational adaptability, increase values of<br />

existing products and services and/or create new knowledge intensive products, processes<br />

and services. As the digital libraries become more knowledge conscious, knowledge<br />

discovery and data mining become essential <strong>for</strong> knowledge management. The in<strong>for</strong>mation<br />

management system should have data mining capabilities <strong>for</strong> helping the knowledge<br />

discovery process and subsequently to evolve as a knowledge management system.<br />

Knowledge<br />

Refinement<br />

Knowledge<br />

Creation<br />

Knowledge<br />

Organization<br />

Knowledge<br />

Integration<br />

Knowledge<br />

Distribution<br />

Knowledge<br />

Application<br />

Fig1. Knowledge Management Process<br />

________________________________________________________________________<br />

Scientific In<strong>for</strong>mation Resource Division, <strong>Indira</strong> <strong>Gandhi</strong> Center <strong>for</strong> <strong>Atomic</strong> <strong>Research</strong>, Kalpakkam-603102 ,<br />

Email:Sound@igcar.ernet.in<br />

141

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