Genetic-based spatial clustering

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 07 Nov 2002Publisher:IEEEJournal:Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), volume 2, pages 953-956

Authors: Antonio Di Nola; Vincenzo Loia; Antonino Staiano;

doi: 10.1109/fuzzy.2000.839162

Genetic-based spatial clustering

- Summary
- Metrics

Abstract

We propose a genetic-level clustering methodology able to cluster objects represented by R/sup p/ spaces. The unsupervised cluster algorithm is based on a fuzzy clustering c-means method that searches the best fuzzy partition of the universe assuming that the evaluation of each object respect to some features is unknown, but knowing that it belongs to circular region of R/sup 2/ space.

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average