Abstract
Efficiency and quality of the product of data mining process is a challenging question for the researchers. Different methods have been proposed in the literature to tackle these problems. Optimization based methods are a way to address this issue. We addressed the problem of data clustering by implementing swarm intelligence based optimization technique called Particle Swarm Optimization (PSO). We scaled the approach to implement it in a hierarchical way using Hierarchical Particle Swarm (HPSO) clustering. The paper also aims to outline our novel outlier detection technique. The research will lead us to provide a benchmark for web usage mining and propose a collective intelligence based recommender system for the usage of Java API documentation.
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Alam, S., Dobbie, G., Riddle, P. (2012). Towards Recommender System Using Particle Swarm Optimization Based Web Usage Clustering. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_27
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DOI: https://doi.org/10.1007/978-3-642-28320-8_27
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