Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 4, April 2013, pg.197 – 204 RESEARCH ARTICLE © 2013, IJCSMC All Rights Reserved 197 AN ADAPTIVE PARTITIONAL CLUSTERING METHOD FOR CATEGORICAL ATTRIBUTE USING K-MEDOID A. Selvakumar 1 1 Assistant Professor of Computer Science, Dept. of Computer Science, Erode, Tamil Nadu, India 1 deesel@rediffmail.com Abstract— partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The operation is needed in a number of data mining tasks such as unsupervised classification and data summation as well as segmentation of large heterogeneous data sets into smaller homogeneous subsets that can be easily managed, separately modeled and analyzed. Clustering is a popular approach used to implement this operation. Partitional clustering attempts to directly decompose the data set into a set of disjoint clusters. More specifically, they attempt to determine an integer number of partitions that optimize as certain criterion function. The criterion function may emphasize the local or global structure of the data and its optimization is an iterative procedure. The intention to analyze the fact that partitional clustering algorithms performs efficiently for numerical attribute rather than categorical attribute. To analyze the algorithm best suits for a matrix data. They work with larger datasets with many attributes. For analysis the Iris dataset has been retrieved from UCI data repository and used in K-Medoid. The outcome of the algorithm is the partition of clusters which can also be visualized in graphical format. The cluster figures differentiate the cluster in various colors with the centroid measure distinctly. Finally it has been determined that K-Medoid is the better partitional algorithm. I. INTRODUCTION The amount of data kept in computer files and databases is growing at a phenomenal rate. At the same time, the users of these data are expecting more sophisticated information from them. Simple structured and query language queries are not adequate to support these increased demands for information. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue. 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 summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. 1.1 METHODOLOGY OF DATA MINING While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored