Vitorino Ramos(*), Ajith Abraham(**), Evolving a Stigmergic Self-Organized
Data-Mining (recently submitted).
http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_50.html

ABSTRACT: Self-organizing complex systems typically are comprised of a
large number of frequently similar components or events. Through their
process, a pattern at the global-level of a system emerges solely from
numerous interactions among the lower-level components of the system.
Moreover, the rules specifying interactions among the system's components
are executed using only local information, without reference to the global
pattern, which, as in many real-world problems is not easily accessible or
possible to be found. Stigmergy, a kind of indirect communication and
learning by the environment found in social insects is a well know example
of self-organization, providing not only vital clues in order to understand
how the components can interact to produce a complex pattern, as can
pinpoint simple biological non-linear rules and methods to achieve improved
artificial intelligent adaptive categorization systems, critical for
Data-Mining. On the present work it is our intention to show that a new
type of Data-Mining can be designed based on Stigmergic paradigms, taking
profit of several natural features of this phenomenon. By hybridizing
bio-inspired Swarm Intelligence with Evolutionary Computation we seek for
an entire distributed, adaptive, collective and cooperative self-organized
Data-Mining. As a real-world / real-time test bed for our proposal,
World-Wide-Web Mining will be used. Having that purpose in mind, Web usage
Data was collected from the Monash University's Web site (Australia), with
over 7 million hits every week. Results are compared to other recent
systems, showing that the system presented is by far promising.

KEYWORDS: Self-organization, Stigmergy, Data-Mining, Linear Genetic
Programming, Distributed and Collaborative Filtering.

(*) CVRM-IST, Technical Univ. Lisbon, PORTUGAL
(**) Natural Comp. Lab, Dep. Comp. Science, Oklahoma Univ, Tulsa, USA.