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Research Article | Open Access |

Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)

Author 1: Kohei Arai Author 2: Cahya Rahmad
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 2, No. 7 · Published 2013

DOI: https://doi.org/10.14569/IJARAI.2013.020701

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Abstract

Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.

Keywords

clustering algorithms mlccd shape independence clustering

How to Cite this Article

Arai, K., & Rahmad, C. (2013). Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other). International Journal of Advanced Research in Artificial Intelligence, 2(7). https://doi.org/10.14569/IJARAI.2013.020701

Arai, Kohei, and Cahya Rahmad. "Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)." International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 7, 2013, https://doi.org/10.14569/IJARAI.2013.020701.

@article{Arai2013,
  title     = {Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {2},
  number    = {7},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Cahya Rahmad},
  doi       = {10.14569/IJARAI.2013.020701},
  url       = {https://doi.org/10.14569/IJARAI.2013.020701}
}

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