Multichannel art network with rule extraction: as a data mining tool

Sayee Sumathi; S. N. Sivanandam; Suresh Babu

doi:10.1117/12.421087

27 March 2001 Multichannel art network with rule extraction: as a data mining tool

Sayee Sumathi, S. N. Sivanandam, Suresh Babu

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421087
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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 amounts of data, pattern classification is a key element in arriving at the solution. Engineering applications especially like medical diagnosis require the ability to accurately classify the recorded data for controlling, tracking and decision making. Although modern technologies enable storage of large streams of data, we do not have technology to help us to understand, analyze, or even visualize the hidden information in the data. Data mining is the emerging field that helps us to understand and analyze the hidden information from Very Large Databases (VLDB). In this paper, two important mining tools (i.e.) Neural networks and Genetic algorithm have been used for mining the database through pattern classification. The processing methodology consists of three major phases: Network construction and training, Pruning, Rule extraction and validation. The networks used are namely Adaptive Resonance Theory 1.5, Adaptive Resonance Theory 3 and Multi Channel ART (MART). These networks belong to the Adaptive Resonance Theory (ART) family, which is a self-organizing neural network capable of clustering arbitrary sequence of input patterns into stable recognition codes. The pruning phase aims at removing redundant links and units without increasing the classification error rate of the network. The pruning methods adopted are Local pruning and Threshold pruning.

Citation Download Citation

Sayee Sumathi, S. N. Sivanandam, and Suresh Babu "Multichannel art network with rule extraction: as a data mining tool", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421087

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Binary data

Data mining

Analog electronics

Image classification

Neural networks

Data storage

Genetic algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years