Validation criteria for enhanced fuzzy clustering

We introduce two new criterions for validation of results obtained from recent novel-clustering algorithm, improved fuzzy clustering (IFC) to be used to find patterns in regression and classification type datasets, separately. IFC algorithm calculates membership values that are used as additional pr...

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Bibliographic Details
Published in:Pattern recognition letters 2008-01, Vol.29 (2), p.97-108
Main Authors: Celikyilmaz, Asli, Burhan Türkşen, I.
Format: Article
Language:English
Subjects:
Applied sciences
Cluster validity index
Exact sciences and technology
Fuzzy clustering
Fuzzy functions
Information, signal and communications theory
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Supervised clustering
Telecommunications and information theory
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Summary:We introduce two new criterions for validation of results obtained from recent novel-clustering algorithm, improved fuzzy clustering (IFC) to be used to find patterns in regression and classification type datasets, separately. IFC algorithm calculates membership values that are used as additional predictors to form fuzzy decision functions for each cluster. Proposed validity criterions are based on the ratio of compactness to separability of clusters. The optimum compactness of a cluster is represented with average distances between every object and cluster centers, and total estimation error from their fuzzy decision functions. The separability is based on a conditional ratio between the similarities between cluster representatives and similarities between fuzzy decision surfaces of each cluster. The performance of the proposed validity criterions are compared to other structurally similar cluster validity indexes using datasets from different domains. The results indicate that the new cluster validity functions are useful criterions when selecting parameters of IFC models.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2007.08.017