References
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 Data Mining. 2016, 1205–1214
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
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
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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We would like to express thanks and gratitude to those who have given their suggestions and opinions in course of preparing this article. This work has no funding source as such and we are grateful for all the mutual discussion and ideas that were conveyed over a period leading towards its accomplishment.
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Bhattacharjee, P., Mitra, P. iMass: an approximate adaptive clustering algorithm for dynamic data using probability based dissimilarity. Front. Comput. Sci. 15, 152314 (2021). https://doi.org/10.1007/s11704-019-9116-y
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DOI: https://doi.org/10.1007/s11704-019-9116-y