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GECCO
2004
Springer

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

11 years 7 months ago
Dynamic and Scalable Evolutionary Data Mining: An Approach Based on a Self-Adaptive Multiple Expression Mechanism
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
Olfa Nasraoui, Carlos Rojas, Cesar Cardona
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Olfa Nasraoui, Carlos Rojas, Cesar Cardona
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