Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data

Abstract

Data mining is a form of analyzing the data which relates the newlinetechniques from fields like statistics machine learning databases artificial newlineintelligence etc Clustering is one of the most important data mining newlinetechniques in which cluster of objects are grouped based on their similarity newlineClustering is the process of accumulating the data records into considerable newlinesubclasses clusters in a way which enhances the relationship within clusters newlineand reduces the similarity among two different clusters The main aim of the newlineclustering is to offer a collection of similar records Data clustering is a newlinecommon methodology used in various fields such as pattern recognition newlineimage analysis and bioinformatics etc newlineIn data analysis clustering and extracting patterns from the data are newlinethe vital process involved in it There are many techniques available for newlinehomogenous data clustering and only very few techniques exist for clustering newlinemixed data items This wants clustering technique for classification of mixed newlinedata Integration of clustering and pattern extraction is natural and necessary newlinetowards a complete and convenient data analysis environment newline newline