Abstract
We present a novel approach to incremental document maps creation, which relies upon partition of a given collection of documents into a hierarchy of homogeneous groups of documents represented by different sets of terms. Further each group (defining in fact separate context) is explored by a modified version of the aiNet immune algorithm to extract its inner structure. The immune cells produced by the algorithm become reference vectors used in preparation of the final document map. Such an approach proves to be robust in terms of time and space requirements as well as the quality of the resulting clustering model.

URL:
http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2006-083

BibTeX:

@inproceedings{REWERSE-RP-2006-083,
	author = {Krzysztof Ciesielski and S\lawomir T. Wierzchoń and Mieczys\law A. K\lopotek},
	title = {An immune network for contextual text data clustering},
	booktitle = {Proceedings of 5th International Conference on Artificial Immune Systems, Oeiras, Portugal (4th--6th September 2006)},
	year = {2006},
	volume = {4163},
	organization = {ICARIS},
	series = {LNCS},
	pages = {432--445},
	url = {http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2006-083}
}