Abstract
Clustering is employed in various fields for analysis and classification. However, the conventional clustering method does not consider changing data. Therefore, in case of change in data, the entire dataset must be re-clustered. A clustering method has been proposed to update the clustering result obtained by a hierarchical clustering method without re-clustering when a point is inserted by using the center and the radius of a cluster. This paper improves this incremental clustering method. By examining the cluster multimodality which is the property of a cluster having several modes, we can select some points of a different distribution inferred from a dendrogram, and transfer the points in the cluster to a different cluster. In addition, when the number of clusters increases, data points previously inserted are updated by re-insertion. Compared with the conventional method, the experimental results demonstrate that the execution time of the proposed method is significantly less and clustering accuracy is comparable for some data.
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Acknowledgements
We are deeply grateful to Mr. Masakazu Ishihara from NITTO SEIKO CO., LTD., who provided us valuable data and discussed them eagerly.
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Narita, K., Hochin, T., Hayashi, Y., Nomiya, H. (2020). Improvement of Incremental Hierarchical Clustering Algorithm by Re-insertion. In: Lee, R. (eds) Computational Science/Intelligence and Applied Informatics. CSII 2019. Studies in Computational Intelligence, vol 848. Springer, Cham. https://doi.org/10.1007/978-3-030-25225-0_8
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DOI: https://doi.org/10.1007/978-3-030-25225-0_8
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