NDLI: Clustering performance of different density function weighted FCM algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xiaofang Liu Chun Yang
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, Sichuan University of Science and Engineering, Zigong, China (Xiaofang Liu) \|\| Institute of Geo-Spatial Information Science and Technology, University of Electronic Science and Technology of China, Chengdu, China (Chun Yang)
Abstract	Fuzzy C-Means (FCM) algorithm is an unsupervised fuzzy clustering method. Clustering results accuracy of the algorithm is affected by equal partition trend of the data sets. When amount of each cluster sample are difference greatly, the optimal solution of the algorithm may not be the correct partition of the data sets. Weighted Fuzzy C-Means (WFCM) algorithm is proposed to overcome this disadvantage. The WFCM algorithm contained a density function which calculates density of each sample by Gaussian function or reciprocal of distance function. The density function solves the problem of equal partition trend to some extent, and also retains favorable convergence and stability for the FCM algorithm. The experiment results are evaluated by the cluster indexes, such as partition coefficient, partition entropy and Xie-Beni index. It shows which weighted function improves the clustering performance of the WFCM algorithm better. When partially supervised information obtained from a few labeled samples is introduced to the WFCM algorithm, the clustering performance of the WFCM algorithm is further enhanced and the convergent speed of objective function is further accelerated.
Starting Page	3296
Ending Page	3300
File Size	353918
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583591
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	weighted fuzzy C-means algorithm Entropy Gaussian function Partitioning algorithms Classification algorithms validity indexes Indexes Iris reciprocal of distance function Clustering algorithms fuzzy clustering Density functional theory fuzzy C-means algorithm partially supervised information
Content Type	Text
Resource Type	Article

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