A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Jiulun Fan,Jing Li

doi:10.4236/am.2014.58119

Abstract

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Highlights

With the development of computer and network technology, the world has entered the age of big data
Since fuzzy set theory was successfully introduced to clustering analysis, it took several important steps until Bezdek reached the alternating optimization (AO) solution of fuzzy clustering, named fuzzy c-means (FCM) clustering algorithm [1]-[3], which improved the partition performance of the previously existing hard c-means clustering (HCM) algorithm, by extending the membership degree from {0,1} to [0,1]
Many researchers have studied the convergence speed and parameter selection of FCM and elaborated various solutions to reduce the execution time [4]-[8]. As another way to speed up the FCM calculations, we proposed an algorithm, named as suppressed fuzzy c-means clustering (S-FCM) algorithm [9], to reduce the execution time of FCM by improving the convergence speed, while preserving its good classification accuracy

Summary

Introduction

With the development of computer and network technology, the world has entered the age of big data. Many researchers have studied the convergence speed and parameter selection of FCM and elaborated various solutions to reduce the execution time [4]-[8] As another way to speed up the FCM calculations, we proposed an algorithm, named as suppressed fuzzy c-means clustering (S-FCM) algorithm [9], to reduce the execution time of FCM by improving the convergence speed, while preserving its good classification accuracy. In order to study this problem, Szilágyi et al defined a new objective function with parameter α and named it optimally suppressed fuzzy c-means (Os-FCM) clustering algorithm [10]-[12]. Huang et al gave Cauchy formula [14], Nyma et al gave exponent formula [15], Li et al gave fuzzy deviation exponent formula [16], and Saad et al gave the clarity formula [17] These selection strategy made the parameter α is changed in each iteration.

Fuzzy C-Means Clustering Algorithm

Suppressed Fuzzy C-Means Clustering Algorithm

The Fixed Selection of Suppression Rate α

Experimental Studies

Synthetic Datasets

Conclusion