Abstract
Clustering is an important unsupervised machine learning method which has played an important role in various fields. As suggested by Alex Rodriguez et al. in a paper published in Science in 2014, the 2D decision graph of the estimated density value versus the minimum distance from the points with higher density values for all the data points can be used to identify the cluster centroids. However, the traditional kernel density estimation methods may be affected by the setting of the parameters and cannot work well for some complex datasets. In this work, a novel potential-based method is designed to estimate density values, which is not sensitive to the parameters and is more effective than the traditional kernel density estimation methods. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in clustering the datasets with various distributions and dimensionalities.
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References
Satyanarayana, A., Acquaviva, V.: Enhanced cobweb clustering for identifying analog galaxies in astrophysics. In: IEEE Electrical and Computer Engineering, pp. 1–4 (2014)
Menéndez, H.D., Plaza, L., Camacho, D.: Combining graph connectivity and genetic clustering to improve biomedical summarization. In: IEEE Congress on Evolutionary Computation, pp. 2740–2747 (2014)
Han, E., et al.: Clustering of 770,000 genomes reveals post-colonial population structure of North America. Nat. Commun. 8, 14238 (2017)
Lu, Y., Wan, Y.: Clustering by sorting potential values (CSPV): a novel potential-based clustering method. Pattern Recogn. 45(9), 3512–3522 (2012)
Kleinberg, J.: An impossibility theorem for clustering. In: NIPS, pp. 463–470 (2002)
Gan, G., Ng, M.K.-P.: k-means clustering with outlier removal. Pattern Recogn. Lett. 8–14 (2017)
Song, H., Yan, H.: Novel K-medoids clustering algorithm based on dynamic search of microparticles under optimized granular computing. Intell. Comput. Appl. (2016)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Serdah, A.M., Ashour, W.M.: Clustering large-scale data based on modified affinity propagation algorithm. J. Artif. Intell. Soft Comput. Res. 6(1), 23–33 (2016)
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Höppner, F.: Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Wiley, New York (1999)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96(34), pp. 226–231 (1996)
Ghassabeh, Y.A., Rudzicz, F.: The mean shift algorithm and its relation to Kernel regression. Inf. Sci. 348, 198–208 (2016)
Campello, R.J.G.B., et al.: Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans. Knowl. Discov. Data (TKDD) 10(1), 5 (2015)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Lu, Y., Hou, X., Chen, X.: A novel travel-time based similarity measure for hierarchical clustering. Neurocomputing 173, 3–8 (2016)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065–1076 (1962)
Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grants No. 61272213) and the Fundamental Research Funds for the Central Universities (Grants No. lzujbky-2016-k07).
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Yan, H., Lu, Y., Li, L. (2017). A Potential-Based Density Estimation Method for Clustering Using Decision Graph. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_9
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DOI: https://doi.org/10.1007/978-3-319-68935-7_9
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