Abstract
The paper deals with the non-distributed and distributed clustering and proposes an agent-based approach to solving the clustering problem instances. The approach is an implementation of the specialized A-Team architecture called JABAT. The paper includes an overview of JABAT and the description of the agent-based algorithms solving the non-distributed and distributed clustering problems. To evaluate the approach the computational experiment involving several well known benchmark instances has been carried out. The results obtained by JABAT-based algorithms are compared with the results produced by the non-distributed and distributed k-means algorithm. It has been shown that the proposed approach produces, as a rule, better results and has the advantage of being scalable, mobile and parallel.
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Czarnowski, I., Jȩdrzejowicz, P. (2009). Agent-Based Non-distributed and Distributed Clustering. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_26
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DOI: https://doi.org/10.1007/978-3-642-03070-3_26
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