Data Mining-Based Variant Subset Features-Reference-Cited by-同舟云学术

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Data Mining-Based Variant Subset Features

Published:2022 Issue: Volume: Page:177-193
ISSN:2522-8595
Container-title:Data Classification and Incremental Clustering in Data Mining and Machine Learning
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Author:

Chakraborty Sanjay,Islam SK Hafizul,Samanta Debabrata

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-93088-2_8

Reference44 articles.

1. Aristidis Likasa, Nikos Vlassis, Jakob J. Verbeek,“The global k-means clustering algorithm”, the journal of the pattern recognition society, Pattern Recognition 36 (2003) 451–461, 2002.

2. Carlos Ordonez, “Clustering Binary Data Streams with K-means”, San Diego, CA, USA. Copyright 2003, ACM 1- 58113-763-x, DMKD’03, June 13, 2003.

3. K. Wang et al., “A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain,” in IEEE Network, vol. 35, no. 1, pp. 204–210, January/February 2021, doi:https://doi.org/10.1109/MNET.011.2000249.

4. Guha, D. Samanta, A. Banerjee and D. Agarwal, “A Deep Learning Model for Information Loss Prevention From Multi-Page Digital Documents,” in IEEE Access, vol. 9, pp. 80451–80465, 2021, doi:https://doi.org/10.1109/ACCESS.2021.3084841.

5. Rohan Kumar, Rajat Kumar, Pinki Kumar, Vishal Kumar, Sanjay Chakraborty, Prediction of Protein-Protein interaction as Carcinogenic using Deep Learning Techniques, 2nd International Conference on Intelligent Computing, Information and Control Systems (ICICCS), Springer, pp. 461–475, 2021.

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