Clustering quality and topology preservation in fast learning SOMs

Title:

Clustering quality and topology preservation in fast learning SOMs

Creator:

Fiannaca, Antonio, Di Fatta, Giuseppe, Rizzo, Riccardo, Urso , Alfonso , and Gaglio, Salvatore

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:6794b47d-37ba-47a4-9afa-d2134f6e0ef6
uuid:6794b47d-37ba-47a4-9afa-d2134f6e0ef6

Subject:

SOM, FLSOM, and Clustering

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.

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 | 2009 Volume:19 | Number:5

Harvested from:

CDK

Metadata only:

false