|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 12 van 13 gevonden artikelen
| Titel: |
Unsupervised fuzzy learning and cluster seeking |
| Auteur: |
A. Bouroumi M. Limouri A. Essa\"{\i}d |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 4 (2001) nr. 3-4 pagina's 241-253 |
| Jaar: |
2001-04-01 |
| Inhoud: |
This paper presents a new approach to unsupervised pattern classification. The classification scheme consists of two main stages. The first one is an unsupervised fuzzy learning procedure, which allows, using a similarity measure and a corresponding threshold, to seek clusters within a set of totally unlabeled samples. It provides, for each detected cluster, a good initial prototype as well as the membership degree of each sample. The second stage is an optimization procedure involving the fuzzy c-means (FCM) algorithm. Both procedures are repeated for different values of the similarity threshold, and three validity criteria are used to assess and rank the quality of all resulting partitions. The effectiveness of this approach is demonstrated, for different parameter values, on both artificial and real test data. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 12 van 13 gevonden artikelen
| << vorige | volgende >> |