Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning

Details Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning

2004-12-17

arxiv.org/abs/cs/0412075v1

in AEB 2002, 1st Spanish Conference on Evolutionary and Bio-Inspired Algorithms, E. Alba, F. Herrera, J.J. Merelo et al. (Eds.

Computer Science

Artificial Intelligence

10 pages, 5 figures, at http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_42.html

Scientific paper

Social insect societies and more specifically ant colonies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases exceed the individual capabilities of a single ant. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy to tackle unsupervised clustering as well as data retrieval problems. The present ant clustering system (ACLUSTER) avoids not only short-term memory based strategies, as well as the use of several artificial ant types (using different speeds), present in some recent approaches. Moreover and according to our knowledge, this is also the first application of ant systems into textual document clustering. KEYWORDS: Swarm Intelligence, Ant Systems, Unsupervised Clustering, Data Retrieval, Data Mining, Distributed Computing, Document Maps, Textual Document Clustering.

Affiliated with

Also associated with

No associations

Rating

Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-159173