MESSAGE PASSING BETWEEN DATA POINT ON GENE EXPRESSIONS FOR CANCER USING CLUSTERING ALGORITHM

Abstract

Clustering (or cluster analysis ) aim to organize a collection of data item in to clusters, such that items within a cluster are more “similar” to each other than they are to item in the other clusters. Clustering is to reduce the amount of data by categorizing or grouping similar data items together. Such grouping is pervasive in the way human’s process information, and one of the motivations for using clustering algorithms is to provide automated tools to help in constructing categories or taxonomies Several years’ different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. The algorithm is tested on both colon and leukemia data set. The experimental results show that affinity propagation outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.