Performance Evaluation of Intelligent Algorithms for Data Mining

Abstract

The exponential growth and success of the digital revolution have ensured that the newlinehuge volumes of data are available all around us. This volume is often mixed, newlineinvolving different data types such as texts, images, hypertexts, videos, graphics newlineetc. interspersed with each other. Discovery of knowledge from this huge volume newlineof data is a challenge indeed. Data Mining refers to the process of extracting newlineknowledgeable information from these huge data. Conventional algorithms such newlineas linear regression, K Means, K Mediods etc. have been widely used to solve newlinedifferent data mining problems. However, these mathematical models have some newlineinherent drawbacks as these are based on certain assumptions, which do not newlinesustain in practice. The current complexity of data and its growing demand newlinenecessitates the development of more advanced and intelligent data mining newlinealgorithms to interpret the information and knowledge from this huge volume of newlinedata. The objective of this research is to design and develop a modified soft newlinecomputing based intelligent algorithm for data mining (data clustering). newline newline