Medical data set analysis - a enchanced clustering approach

Abstract

Clustering is the process of organizing data objects into a set of disjoint classes called clusters. Clustering is an example of unsupervised classification. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups Cluster analysis is one of the primary data analysis methods and Fuzzy C-means is one of the most well known popular clustering algorithms.The Fuzzy C-means algorithm is one of the frequently used clustering methods in data mining, due to its performance in clustering massive data sets. The final clustering result of the Fuzzy C-means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. A new method Bacteria Foraging Optimization Algorithm (BFOA) and Ant Colony Optimization (ACO) with Fuzzy C-means is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity.The proposed algorithm has the more accuracy with less computational time comparatively original k-means clustering algorithm. In this research works aims to select the initial cluster from BFOA and ACO, then, in after several iteration of the algorithm, for analyze the medical dataset. The final result converges to actual cluster center achieved and it is very important for an FCM algorithm.