Cluster analysis is a method that categorizes data by abstracting essential structure either as a grouping of objects. Every cluster consists of objects that are similar between themselves and dissimilar to objects of other groups. Computing similarity in clustering is the most required task in the real world environment which needs to process well to attain the improved cluster pattern. To solve this kind of problem, this paper presents a Fuzzy based joint intellect of Colonies with vector quantization (FJIC-VQ) algorithm performs two structures of data analysis that can be used to extract cluster points describing important cluster features or to predict future data trends. The proposed method follows a Ant colony building and Vector quantization method is to accurately calculate the distance measures for all case in the data. Finally, the FJIC-VQ algorithm performs the probabilistic clustering model in the distributed manner. The achieved FJIC-VQ has higher cluster quality variation with less computation time when comparing to K-means, Average link and PACE algorithms.
Volume 11 | 06-Special Issue
Pages: 811-819