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Abstract:

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 known example of self-organization, providing not only vital clues in order to understand how the components can interact to produce a complex pattern but also can pinpoint simple biological non-linear rules and methods to achieve improved artificial intelligent adaptive categorization systems, critical for data mining applications. In this paper we attempt to show that a new type of data mining paradigm can be designed based on Stigmergic paradigms by combining the profit of several natural features of this phenomenon. We further propose hybrid bio-inspired swarm intelligence with evolutionary computation for an entirely distributed, adaptive, collective and cooperative selforganized data mining. The proposed framework is validated using Web mining data. Comparison of empirical results with other intelligent paradigms shows that the system presented is very promising. I.

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