research-article

Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering

Authors:

I. GathAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 16, Issue 8

Pages 855 - 861

https://doi.org/10.1109/34.308484

Published: 01 August 1994 Publication History

Abstract

A new fuzzy clustering algorithm, designed to detect and characterize ring-shaped clusters and combinations of ring-shaped and compact spherical clusters, has been developed. This FKR algorithm includes automatic search for proper initial conditions in the two cases of concentric and excentric (intersected) combinations of clusters. Validity criteria based on total fuzzy area and fuzzy density are used to estimate the optimal number of substructures in the data set. The FKR algorithm has been tested on a variety of simulated combinations of ring-shaped and compact spherical clusters, and its performance proved to be very good, both in identifying the input shapes and in recovering the input parameters. Application of the FKR algorithm to an MRI image of the heart's left ventricle was used to investigate the possibility of using this algorithm as an aid in image processing.

References

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Digital Library

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Cited By

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Esfahanipour AAghamiri W(2010)Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2009.11.02037:7(4742-4748)Online publication date: 1-Jul-2010
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Wang T(2009)Possibilistic shell clustering of template-based shapesIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2008.92436017:4(777-793)Online publication date: 1-Aug-2009
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Index Terms

Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Index terms have been assigned to the content through auto-classification.

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 16, Issue 8

August 1994

105 pages

ISSN:0162-8828

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Copyright © Copyright © 1994 IEEE. All Rights Reserved.

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IEEE Computer Society

United States

Publication History

Published: 01 August 1994

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Cited By

Mimaroglu SErdil E(2011)ASODEngineering Applications of Artificial Intelligence10.1016/j.engappai.2011.07.00524:7(1295-1299)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.engappai.2011.07.005
Esfahanipour AAghamiri W(2010)Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2009.11.02037:7(4742-4748)Online publication date: 1-Jul-2010
https://dl.acm.org/doi/10.1016/j.eswa.2009.11.020
Wang T(2009)Possibilistic shell clustering of template-based shapesIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2008.92436017:4(777-793)Online publication date: 1-Aug-2009
https://dl.acm.org/doi/10.1109/TFUZZ.2008.924360
Su MSu SZhao Y(2009)A swarm-inspired projection algorithmPattern Recognition10.1016/j.patcog.2009.03.02042:11(2764-2786)Online publication date: 1-Nov-2009
https://dl.acm.org/doi/10.1016/j.patcog.2009.03.020
Shieh HYang YChang PJeng J(2009)Robust neural-fuzzy method for function approximationExpert Systems with Applications: An International Journal10.1016/j.eswa.2008.08.07236:3(6903-6913)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1016/j.eswa.2008.08.072
Zhang YWang WZhang XLi Y(2008)A cluster validity index for fuzzy clusteringInformation Sciences: an International Journal10.1016/j.ins.2007.10.004178:4(1205-1218)Online publication date: 20-Feb-2008
https://dl.acm.org/doi/10.1016/j.ins.2007.10.004
Shah JSalim N(2007)A soft hierarchical algorithm for the clustering of multiple bioactive chemical compoundsProceedings of the 1st international conference on Bioinformatics research and development10.5555/1762370.1762386(140-153)Online publication date: 12-Mar-2007
https://dl.acm.org/doi/10.5555/1762370.1762386
Bandyopadhyay SSaha S(2007)GAPSPattern Recognition10.1016/j.patcog.2007.03.02640:12(3430-3451)Online publication date: 1-Dec-2007
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Yang XCao ASong Q(2006)A New Cluster Validity for Data ClusteringNeural Processing Letters10.1007/s11063-006-9005-x23:3(325-344)Online publication date: 1-Jun-2006
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Yang XSong QZhang WWang Z(2006)Clustering spherical shells by a mini-max information algorithmProceedings of the 7th Asian conference on Computer Vision - Volume Part II10.1007/11612704_23(224-233)Online publication date: 13-Jan-2006
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