Assigned Elementary Centroids Thoughtfully in K-Medoids Algorithm

With the tremendous development in all areas of scientific, economic, political and other appeared the need to find nontraditional ways in which to deal with all the data patterns (text, video and audio, etc.), which are becoming very large volumes these days. Was necessary to find new ways to develop knowledge and information hidden within this huge amount of data such as query for customers who have habits of purchasing the same or prospects for the sale of a particular commodity in one of the geographical areas and other queries deductive and based on the technology of data mining. The process of exploration in several of the most important methods of clustering method (assembly) Clustering, which are several algorithms. We will focus in this research on the use of a way calculated to create centers of First Instance of the algorithm K-Medoids which is based on the principle of the division of data into clusters each cluster contains a replica database easy to handle, rather than selected as random which in turn leads to the emergence of different results and slow in the implementation of the algorithm.

Improve K-Means Algorithm

The algorithm classifies objects to a predefined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each other. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This research focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This research uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid.