Ant Colony Optimization based clustering methodology

Date

2015-03-01

Author

İNKAYA, TÜLİN
Kayaligil, Sinan
Özdemirel, Nur Evin

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In this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two objective functions, namely adjusted compactness and relative separation. Each objective function evaluates the clustering solution with respect to the local characteristics of the neighborhoods. This allows us to measure the quality of a wide range of clustering solutions without a priori information. Next, using the two objective functions we present a novel clustering methodology based on Ant Colony Optimization (ACO-C). ACO-C works in a multi-objective setting and yields a set of non-dominated solutions. ACO-C has two pre-processing steps: neighborhood construction and data set reduction. The former extracts the local characteristics of data points, whereas the latter is used for scalability. We compare the proposed methodology with other clustering approaches. The experimental results indicate that ACO-C outperforms the competing approaches. The multi-objective evaluation mechanism relative to the neighborhoods enhances the extraction of the arbitrary-shaped clusters having density variations.

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URI

https://hdl.handle.net/11511/39041

Journal

APPLIED SOFT COMPUTING

DOI

https://doi.org/10.1016/j.asoc.2014.11.060

Collections

Department of Industrial Engineering, Article

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Citation Formats

T. İNKAYA, S. Kayaligil, and N. E. Özdemirel, “Ant Colony Optimization based clustering methodology,” APPLIED SOFT COMPUTING, pp. 301–311, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39041.