  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.
