Abstract
As a clustering approach with significant potential, the density peak (DP) clustering algorithm is shown to be adapted to different types of datasets. This algorithm is developed on the basis of a few simple assumptions. While being simple, this algorithm performs well in many experiments. However, we find that local density is not very informative in identifying cluster centers and may be one reason for the influence of density parameter on clustering results. For the purpose of solving this problem and improving the DP algorithm, we study the cluster center identification process of the DP algorithm and find that what distinguishes cluster centers from non-density-peak data is not the great local density, but the role of density peaks. We then propose to describe the role of density peaks based on the local density of subordinates and present a better alternative to the local density criterion. Experiments show that the new criterion is helpful in isolating cluster centers from the other data. By combining this criterion with a new average distance based density kernel, our algorithm performs better than some other commonly used algorithms in experiments on various datasets.
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Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045, and by the Natural Science Foundation of Liaoning Province under Grant No. 20170540013 and 20170540005.
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Hou, J., Zhang, A., Lv, C., E, X. (2018). Enhancing Cluster Center Identification in Density Peak Clustering. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_24
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DOI: https://doi.org/10.1007/978-3-319-99365-2_24
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