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Local modes-based free-shape data partitioning

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Published
Publication date9/12/2016
Host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE
Number of pages8
ISBN (Electronic)9781509042401
ISBN (Print)9781509042418
Original languageEnglish
EventIEEE SSCI 2016 - ATHENS, Greece
Duration: 6/12/20169/12/2016

Conference

ConferenceIEEE SSCI 2016
CountryGreece
CityATHENS
Period6/12/169/12/16

Conference

ConferenceIEEE SSCI 2016
CountryGreece
CityATHENS
Period6/12/169/12/16

Abstract

In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.