iMass: an approximate adaptive clustering algorithm for dynamic data using probability based dissimilarity

Panthadeep BHATTACHARJEE , Pinaki MITRA

Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (2) : 152314

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Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (2) : 152314 DOI: 10.1007/s11704-019-9116-y
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iMass: an approximate adaptive clustering algorithm for dynamic data using probability based dissimilarity

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Panthadeep BHATTACHARJEE, Pinaki MITRA. iMass: an approximate adaptive clustering algorithm for dynamic data using probability based dissimilarity. Front. Comput. Sci., 2021, 15(2): 152314 DOI:10.1007/s11704-019-9116-y

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References

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Ting K M, Zhu Y, Carman M, Zhu Y, Zhou Z H. Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure. In: Proceedings of the 22nd ACM International Conference on Knowledge Discovery and DataMining. 2016, 1205–1214

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Ester M, Kriegel H P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 1996, 226–231

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Aryal S, Ting K M, Haffari G, Washio T. Mp-dissimilarity: a data dependent dissimilarity measure. In: Proceedings of the IEEE International Conference on Data Mining. 2014, 707–712

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Ting K M, Zhou G T, Liu F T, Tan S C. Mass estimation. Journal of Machine Learning, 2013, 90(1), 127–160

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