Hybrid fuzzy clustering using LP norms

Tomasz Przybyła; Janusz Jezewski; Krzysztof Horoba; Dawid Roj

doi:10.1007/978-3-642-20039-7_19

Hybrid fuzzy clustering using L_P norms

Tomasz Przybyła
, Janusz Jezewski
, Krzysztof Horoba
, Dawid Roj

Katedra Cybernetyki, Nanotechnologii i Przetwarzania Danych (RAU/RAU10)

Institute of Medical Technology and Equipment

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

4 Cytowania z bazy Scopus

Abstrakt

The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a value that minimizes introducted generalized cost function. The generalized cost function utilizes the L _p norm. The fuzzy meridian is a special case of cluster prototype for p = 2 as well as the fuzzy meridian for p = 1. A method for the norm selection is proposed. An example illustrating the performance of the proposed method is given.

Język oryginału	angielski
Tytuł publikacji goszczącej	Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings
Wydawca	Springer Verlag
Strony	187-196
Liczba stron	10
Edycja	PART 1
ISBN (drukowany)	9783642200380
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-20039-7_19
Status publikacji	Opublikowano - 2011
Wydarzenie	3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011 - Daegu, Korea, Republika Czas trwania: 20 kwi 2011 → 22 kwi 2011

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Numer	PART 1
Tom	6591 LNAI
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Konferencja

Konferencja	3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011
Kraj/Terytorium	Korea, Republika
Miejscowość	Daegu
Okres	20/04/11 → 22/04/11

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-642-20039-7_19

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Przybyła, T., Jezewski, J., Horoba, K., & Roj, D. (2011). Hybrid fuzzy clustering using L_P norms. W Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings (PART 1 red., str. 187-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 6591 LNAI, Nr PART 1). Springer Verlag. https://doi.org/10.1007/978-3-642-20039-7_19

Przybyła, Tomasz ; Jezewski, Janusz ; Horoba, Krzysztof i in. / Hybrid fuzzy clustering using L_P norms. Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings. PART 1. red. Springer Verlag, 2011. str. 187-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{ceb937dee1aa4a80a7b5289717d27f87,

title = "Hybrid fuzzy clustering using LP norms",

abstract = "The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a value that minimizes introducted generalized cost function. The generalized cost function utilizes the L p norm. The fuzzy meridian is a special case of cluster prototype for p = 2 as well as the fuzzy meridian for p = 1. A method for the norm selection is proposed. An example illustrating the performance of the proposed method is given.",

author = "Tomasz Przyby{\l}a and Janusz Jezewski and Krzysztof Horoba and Dawid Roj",

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Przybyła, T, Jezewski, J, Horoba, K & Roj, D 2011, Hybrid fuzzy clustering using L_P norms. w Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings. PART 1 wyd., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), nr PART 1, tom 6591 LNAI, Springer Verlag, str. 187-196, 3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011, Daegu, Korea, Republika, 20/04/11. https://doi.org/10.1007/978-3-642-20039-7_19

Hybrid fuzzy clustering using L_P norms. / Przybyła, Tomasz; Jezewski, Janusz; Horoba, Krzysztof i in.
Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings. PART 1. red. Springer Verlag, 2011. str. 187-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 6591 LNAI, Nr PART 1).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

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AU - Roj, Dawid

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N2 - The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a value that minimizes introducted generalized cost function. The generalized cost function utilizes the L p norm. The fuzzy meridian is a special case of cluster prototype for p = 2 as well as the fuzzy meridian for p = 1. A method for the norm selection is proposed. An example illustrating the performance of the proposed method is given.

AB - The fuzzy clustering methods are useful in the data mining applications. This paper describes a new fuzzy clustering method in which each cluster prototype is calculated as a value that minimizes introducted generalized cost function. The generalized cost function utilizes the L p norm. The fuzzy meridian is a special case of cluster prototype for p = 2 as well as the fuzzy meridian for p = 1. A method for the norm selection is proposed. An example illustrating the performance of the proposed method is given.

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Przybyła T, Jezewski J, Horoba K, Roj D. Hybrid fuzzy clustering using L_P norms. W Intelligent Information and Database Systems - Third International Conference, ACIIDS 2011, Proceedings. PART 1 red. Springer Verlag. 2011. str. 187-196. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-20039-7_19