Design of soft computing models for data mining applications

Abstract

Although modern technologies enable storage of large streams of data, but there is no technology which can help to understand, analyze and visualize the hidden information in the data. Data mining also called as data or knowledge

discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarizes the relationships identified. Pattern classification is one particular category of data mining, which enables the discovery of knowledge from very large databases (VLDB). Data mining can be applied to a wide range of applications such as business forecasting, decision support systems, SONAR, RADAR, SEISMIC

and medical diagnosis.<BR>

Artificial neural networks are used to mine the database which has better noise immunity and lesser training time. A self-organizing neural network architecture called predictive ART or ARTMAP is introduced that is capable of fast stable learning, hypothesis testing in response to arbitrary stream of input patterns. A generalization of binary ARTMAP is the fuzzy ARTMAP, which learns to classify input by a pattern of fuzzy membership values between 0 and 1, indicating the extent to which each feature is present. Generalization of fuzzy ARTMAP is the Cascade ARTMAP which has pre-existing symbolic rules that are used to initialize the network before learning so that the network efficiency is increased. This rule insertion also provides knowledge to the network that cannot be captured by training examples. Interpretation of knowledge learned by this neural network leads to compact and simpler rules compared to Back propagation approach. Another selforganizing algorithm is proposed using Kohonen Architecture which also requires lesser time and high prediction accuracy compared to BPN. Moreover, the rules extracted from this network are very simple compared to BPN approach. Finally, the extracted rules have been validated for their correctness. This approach is most widely used in the Medical Industry for correct prediction when the database is large in size. At this time, the manual mining on such a voluminous data is very difficult and also a very time consuming process. Sometimes it may lead to incorrect predictions. Henceforth, the data mining software is developed. The performance evaluation of all three networks namely, Cascade ARTMAP, Fuzzy ARTMAP and Kohonen have been done and compared with conventional methods. Simulation is carried out using the medical data bases taken from the UCI repository of machine learning data bases. The developed data mining software can also be used for other applications like Web, communcations, and pattern recognition.