An efficient k'-means clustering algorithm

Rizman Žalik, Krista

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Naslov:	An efficient k'-means clustering algorithm
Avtorji:	ID Rizman Žalik, Krista (Avtor)
Datoteke:	http://dx.doi.org/10.1016/j.patrec.2008.02.014
Jezik:	Angleški jezik
Vrsta gradiva:	Neznano
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Opis:	This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm.
Ključne besede:	algorithms, clustering analysis, k-means, cost-function, rival penalized mechanism, datasets
Leto izida:	2008
PID:	20.500.12556/DKUM-26141
UDK:	004.93
COBISS.SI-ID:	12121366
ISSN pri članku:	0167-8655
NUK URN:	URN:SI:UM:DK:F7IENQHK
Datum objave v DKUM:	31.05.2012
Število ogledov:	2690
Število prenosov:	129
Metapodatki:
Področja:	Ostalo
:	RIZMAN ŽALIK, Krista, 2008, An efficient k’-means clustering algorithm. Pattern recognition letters [na spletu]. 2008. [Dostopano 2 april 2025]. Pridobljeno s: http://dx.doi.org/10.1016/j.patrec.2008.02.014 Kopiraj citat

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Gradivo je del revije

Naslov:	Pattern recognition letters
Skrajšan naslov:	Pattern recogn. lett.
Založnik:	North-Holland
ISSN:	0167-8655
COBISS.SI-ID:	26103296

Sekundarni jezik

Jezik:	Angleški jezik
Ključne besede:	cluster analiza, algoritmi, obdelava podatkov

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