[Swarm-Modelling] Evolving a Self-Organized Data-Mining

[Vitorino RAMOS]

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.