Abstract
In this paper, we focus on the class of graph-based clustering models, such as growing neural gas or idiotypic nets for the purpose of high-dimensional text data clustering. We present a novel approach, which does not require operation on the complex overall graph of clusters, but rather allows to shift majority of effort to context-sensitive, local subgraph and local sub-space processing. Savings of orders of magnitude in processing time and memory can be achieved, while the quality of clusters is improved, as presented experiments demonstrate.

URL:
http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2006-145

BibTeX:

@inproceedings{REWERSE-RP-2006-145,
	author = {Krzysztof Ciesielski and Mieczys\law A. K\lopotek},
	title = {Text Data Clustering by Contextual Graphs},
	booktitle = {Proceedings of Ninth International Conference on Discovery Science, Barcelona, Spain (7th--10th October 2006)},
	year = {2006},
	volume = {4265},
	series = {LNAI},
	pages = {65--76},
	url = {http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2006-145}
}