Ideal type model and an associated method for relational fuzzy clustering

Susana Nascimento, Boris Mirkin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The ideal type model by Mirkin and Satarov (1990) expresses data points as convex combinations of some 'ideal type' points. However, this model cannot prevent the ideal type points being far away from the observations and, in fact, requires that. Archetypal analysis by Cutler and Breiman (1994) and proportional membership fuzzy clustering by Nascimento et al. (2003) propose different ways of avoiding this entrapment. We propose one more way out - by assuming the ideal types being mutually orthogonal and transforming the model by multiplying it over its transpose. The obtained additive fuzzy clustering model for relational data is akin to that more recently analysed by Mirkin and Nascimento (2012) in a different context. The one-by-one clustering approach to the ideal type model is reformulated here as that naturally leading to a spectral clustering algorithm for finding fuzzy membership vectors. The algorithm is proven to be computationally valid and competitive against popular relational fuzzy clustering algorithms.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5090-6034-4
DOIs
Publication statusPublished - 23 Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
Duration: 9 Jul 201712 Jul 2017

Conference

Conference2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
CountryItaly
CityNaples
Period9/07/1712/07/17

Keywords

  • C-MEANS

Fingerprint Dive into the research topics of 'Ideal type model and an associated method for relational fuzzy clustering'. Together they form a unique fingerprint.

Cite this