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Proceedings Paper

New soft computing model for data mining
Author(s): Sayee Sumathi; S. N. Sivanandam; Suresh Babu
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Paper Abstract

Information and energy are at the core of everything around us. Our entire existence is a process of gathering, analyzing, understanding and acting on the information. For many applications dealing with large amount of data, pattern classification is a key element in arriving at the solution. Engineering applications like SONAR, RADAR, SEISMIC and medical diagnosis require the ability to accurately classify the recorded data for controlling, tracking and decision making respectively. Although modern technologies enable storage of large streams of data, we do not yet have a technology to help us to understand, analyze, or even visualize the hidden information in the data. Data Mining is now the emerging field attracting all research communities. Pattern classification is one particular category of Data Mining, which enables the Discovery of Knowledge from Very Large Databases (VLDB). Modern developments in hardware and software technologies have paved way for developing the software for analyzing and visualizing the data. This development is based on the application of data mining concept. Artificial Neural Networks are used to mine the data base which has better noise immunity and less training time. The paper aims mainly at classification accuracy with reduction in learning time using self-organizing neural networks. The newness in the concept of Adaptive Resonance Theory (ART) makes this as the best and efficient approach for classification.

Paper Details

Date Published: 27 March 2001
PDF: 11 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421073
Show Author Affiliations
Sayee Sumathi, PSG College of Technology (India)
S. N. Sivanandam, PSG College of Technology (India)
Suresh Babu, PSG College of Technology (India)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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