  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.
