Enhancing Structure Discovery for Data Mining in Graphical Databases Using Evolutionary Programming

Sanghamitra Bandyopadhyay, Ujjwal Maulik, Diane J. Cook, Lawrence B. Holder, and Yousuf Ajmerwala

The purpose of this paper is to develop an evolutionary programming based system that performs data mining on databases represented as graphs. The importance of such an endeavor can hardly be overemphasized, given that much of the data collected nowadays is structural in nature, or is composed of parts and relations between the parts, which can be naturally represented as graphs. The searching capability of evolutionary programming is utilized for discovering concepts or substructures that are often repeating in such structural data. The superiority of the proposed technique over the previously developed Subdue system, which uses a computationally constrained beam search in the space of substructures, is demonstrated for a number of data sets in the Web domain.

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