Fuzzy K-Minpen Clustering and K-nearest-minpen Classification Procedures Incorporating Generic Distance-Based Penalty Minimizers

Anna Cena , Marek Gągolewski

Abstract

We discuss a generalization of the fuzzy (weighted) k-means clustering procedure and point out its relationships with data aggregation in spaces equipped with arbitrary dissimilarity measures. In the proposed setting, a data set partitioning is performed based on the notion of points’ proximity to generic distance-based penalty minimizers. Moreover, a new data classification algorithm, resembling the k-nearest neighbors scheme but less computationally and memory demanding, is introduced. Rich examples in complex data domains indicate the usability of the methods and aggregation theory in general.
Author Anna Cena (FMIS / DIE) - [Instytut Badań Systemowych Polskiej Akademii Nauk]
Anna Cena,,
- Department of Integral Equations
- Instytut Badań Systemowych Polskiej Akademii Nauk
, Marek Gągolewski (FMIS / DIE) - [Systems Research Institute (IBS PAN) [Polish Academy of Sciences (PAN)]]
Marek Gągolewski,,
- Department of Integral Equations
- Instytucie Badań Systemowych
Pages445-456
Publication size in sheets0.55
Book Carvalho Joao Paulo, Lesot Marie-Jeanne, Kaymak Uzay, Vieira Susana, Bouchon-Meunier Bernadette, Yager Ronald R. (eds.): INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2016, PT II, Communications in Computer and Information Science, vol. 611, 2016, SPRINGER INT PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, SPRINGER INT PUBLISHING AG, ISBN 978-3-319-40581-0, [ 978-3-319-40580-3]
Keywords in EnglishFuzzy k-means algorithm, Clustering, Classification, Fusion functions, Penalty minimizers
ASJC Classification2600 General Mathematics; 1700 General Computer Science
Abstract in PolishZaproponowaliśmy uogólnienie ważonej (rozmytej) procedury analizy skupień typu k-średnich oraz klasyfikacji opartej na k-prototypach. W przedstawionych algorytmach zamiast centroidów w R^d zastosowaliśmy dowolne funkcje agregujące oparte na funkcji straty generowanej przez miary odmienności. Dzięki temu można je zastosować na różnego rodzaju danych, m.in. ciągach DNA, obrazach, sygnałach audio, szeregach czasowych itp.
DOIDOI:10.1007/978-3-319-40581-0_36
URL http://link.springer.com/chapter/10.1007%2F978-3-319-40581-0_36
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 26-01-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 26-01-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 2; WoS Citations = 2; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.371
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