Poisson distribution based initialization for fuzzy clustering

Title:

Poisson distribution based initialization for fuzzy clustering

Creator:

Vintr, Tomas, Vintrova, Vanda, and Rezankova , Hana

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:92efd5dc-e190-4526-8f76-77d4d8aaf369
uuid:92efd5dc-e190-4526-8f76-77d4d8aaf369
doi:10.14311/NNW.2012.22.009

Subject:

Poisson distribution based initialization, k-means, fuzzy c-means, possibilistic clustering algorithm, and unsupervised possibilistic fuzzy clustering

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic clustering algorithm) and UPFC (unsupervised possibilistic fuzzy clustering) with different initializations. These experiments demonstrate an improvement in the quality of clustering when initialized by the proposed method. The concept how to estimate a ratio of added noise is also presented.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:2

Harvested from:

CDK

Metadata only:

false