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Improved robust fuzzy clustering algorithm
University of Toronto.
University of Toronto.ORCID iD: 0000-0003-4977-6339
University of Toronto.
1999 (English)In: 1999 IEEE International Fuzzy Systems Conference Proceedings, Seoul, South Korea, 22-25 August 1999, Piscataway, NJ: IEEE Communications Society, 1999, Vol. 3Conference paper, Published paper (Refereed)
Abstract [en]

Fuzzy c-means (FCM) and hard clustering algorithms are the most common tools for data partitioning. However, these clustering algorithms may fail completely in the presence of noise. In this paper, we introduce a robust noise rejection clustering algorithm based on a combination of techniques that treat the FCM weak points with a traditional noise rejection algorithm. Unlike the traditional FCM, the proposed algorithm is a powerful tool for partitioning the data in the presence of noise (outliers).

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 1999. Vol. 3
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Onboard space systems
Identifiers
URN: urn:nbn:se:ltu:diva-33195DOI: 10.1109/FUZZY.1999.790082Scopus ID: 33280880Local ID: 7ff4348b-fd3a-4c84-bcaf-cf4446398a2fOAI: oai:DiVA.org:ltu-33195DiVA, id: diva2:1006432
Conference
IEEE International Conference on Fuzzy Systems : 22/08/1999 - 25/08/1999
Note
Upprättat; 1999; 20150108 (ninhul)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-12-15Bibliographically approved

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Emami, Reza

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