Dynamic and Scalable Evolutionary Data Mining: An Approach Based on a Self-Adaptive Multiple Expression Mechanism

Olfa Nasraoui, Carlos Rojas, and Cesar Cardona

Abstract. Data mining has recently attracted attention as a set of efficient techniques that can discover patterns from huge data. More recent advancements in collecting massive evolving data streams created a crucial need for dynamic data mining. In this paper, we present a genetic algorithm based on a new representation mechanism, that allows several phenotypes to be simultaneously expressed to different degrees in the same chromosome. This gradual multiple expression mechanism can offer a simple model for a multiploid representation with self-adaptive dominance, including co-dominance and incomplete dominance. Based on this model, we also propose a data mining approach that considers the data as a reflection of a dynamic environment, and investigate a new evolutionary approach based on continuously mining non-stationary data sources that do not fit in main memory. Preliminary experiments are performed on real Web clickstream data.

LNCS 3102, p. 1401 ff.

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