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iMass: an approximate adaptive clustering algorithm for dynamic data using probability based dissimilarity |
Panthadeep BHATTACHARJEE( ), Pinaki MITRA() |
| Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati 781039, India |
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Corresponding Author(s):
Panthadeep BHATTACHARJEE
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Just Accepted Date: 17 December 2019
Issue Date: 10 October 2020
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K M Ting, Y Zhu, M Carman, Y Zhu, Z H Zhou. 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
https://doi.org/10.1145/2939672.2939779
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M Ester, H P Kriegel, J Sander, X Xu. 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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S Aryal, K M Ting, G Haffari, T Washio. Mp-dissimilarity: a data dependent dissimilarity measure. In: Proceedings of the IEEE International Conference on Data Mining. 2014, 707–712
https://doi.org/10.1109/ICDM.2014.33
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K M Ting, G T Zhou, F T Liu, S C Tan. Mass estimation. Journal of Machine Learning, 2013, 90(1), 127–160
https://doi.org/10.1007/s10994-012-5303-x
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