Fuzzy clustering with volume prototypes and adaptive cluster merging

Kaymak, U; Setnes, M

Abstract

Two extensions to the objective function-based fuzzy
clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters
is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to
Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples.

Citation

Kaymak, U., & Setnes, M. Fuzzy clustering with volume prototypes and adaptive cluster merging. IEEE Transactions on Fuzzy Systems, 10(6), 705-712. https://doi.org/10.1109/TFUZZ.2002.805901

• Journal Article Type: Article
• Deposit Date: Mar 12, 2009
• Publicly Available Date: Mar 12, 2009
• Journal: IEEE Transactions on Fuzzy Systems
• Print ISSN: 1063-6706
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 6
• Pages: 705-712
• DOI: https://doi.org/10.1109/TFUZZ.2002.805901
• Keywords: Cluster merging, fuzzy clustering, similarity,; volume prototypes
• Publisher URL: http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1097771
• Related Public URLs: http://ieeexplore.ieee.org/Xplore/dynhome.jsp