Improved Inter-Cluster Separation Algorithm

Abstract:

The inter-cluster separation (ICS) algorithm adds the separation item into the objective function to minimize the fuzzy Euclidean distance and maximize the inter-cluster separation. However, ICS is sensitive to noisy data, so an improved inter-cluster separation (IICS) algorithm is proposed to deal with this problem. It is claimed that IICS is an incorporation of ICS and improved possibilistic c-means (IPCM) clustering. IICS can produce both possibilities and memberships simultaneously, and it overcomes the noise sensitivity problem of ICS and the coincident clusters problem of possibilistic c-means (PCM) clustering. Further, IICS does not depend on the parameters that exist in IPCM. The experimental results show that IICS compares favorably with ICS.

• Periodical: Key Engineering Materials (Volumes 439-440)
• Pages: 361-366
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.439-440.361
• Online since: June 2010
• Authors: Xiao Hong Wu, Bin Wu, Jie Wen Zhao
• Keywords: Fuzzy Clustering, Fuzzy C-Means (FCM), Inter-Cluster Separation, Possibilistic c-means

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