Ranking overlap and outlier points in data using soft kernel spectral clustering

RaghvenPhDa Mall, Rocco Langone, Johan A.K. Suykens

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

Abstract

Soft clustering algorithms can handle real-life datasets better as they capture the presence of inherent overlapping clusters. A soft kernel spectral clustering (SKSC) method proposed in [1] exploited the eigen-projections of the points to assign them different cluster membership probabilities. In this paper, we detect points in dense overlapping regions as overlap points. We also identify the outlier points by exploiting the eigen-projections. We then propose novel ranking techniques using structure and similarity properties in the eigen-space to rank these overlap and outlier points. By ranking the overlap and outlier points we provide an order for the most and least influential points in the dataset. We demonstrate the effectiveness of our ranking measures on several datasets.

Original languageEnglish
Title of host publication23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings
Publisheri6doc.com publication
Pages537-542
Number of pages6
ISBN (Electronic)9782875870148
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Bruges, Belgium
Duration: 22 Apr 201524 Apr 2015

Other

Other23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015
CountryBelgium
CityBruges
Period22/4/1524/4/15

Fingerprint

Clustering algorithms

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Mall, R., Langone, R., & Suykens, J. A. K. (2015). Ranking overlap and outlier points in data using soft kernel spectral clustering. In 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings (pp. 537-542). i6doc.com publication.

Ranking overlap and outlier points in data using soft kernel spectral clustering. / Mall, RaghvenPhDa; Langone, Rocco; Suykens, Johan A.K.

23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings. i6doc.com publication, 2015. p. 537-542.

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

Mall, R, Langone, R & Suykens, JAK 2015, Ranking overlap and outlier points in data using soft kernel spectral clustering. in 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings. i6doc.com publication, pp. 537-542, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015, Bruges, Belgium, 22/4/15.
Mall R, Langone R, Suykens JAK. Ranking overlap and outlier points in data using soft kernel spectral clustering. In 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings. i6doc.com publication. 2015. p. 537-542
Mall, RaghvenPhDa ; Langone, Rocco ; Suykens, Johan A.K. / Ranking overlap and outlier points in data using soft kernel spectral clustering. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings. i6doc.com publication, 2015. pp. 537-542
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