Approach to collaborative fuzzy clustering in large data analysis

Authors

  • Mai Dinh Sinh
  • Dang Trong Hop
  • Do Viet Duc
  • Ngo Thanh Long

DOI:

https://doi.org/10.54654/isj.v2i17.892

Keywords:

GPUs, collaborative clustering, fuzzy clustering, high-performance computing

Tóm tắt

Abstract— When data sets have one or more similar characteristics, the clustering in each of these data sets will have an effect on the other data sets. However, for various reasons such as data security issues, these data cannot be stored centrally but in different places. Collaborative clustering is a clustering technique that allows to performance of local clustering on each sub-data set and to exchange of information with other data sets. A collaborative process will be performed to adjust the clustering results on each subset to achieve better clustering results on the subsets of data. This paper presents a collaborative fuzzy clustering approach in big data analysis based on a high-performance computational model to improve the computation speed. Experiments on the Kitsune network attack dataset show that the proposed algorithm significantly improves the calculation speed compared to the previous method.

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Published

2023-03-31

How to Cite

Sinh, M. D., Hop, D. T., Duc, D. V., & Long, N. T. (2023). Approach to collaborative fuzzy clustering in large data analysis. Journal of Science and Technology on Information Security, 3(17), 10-16. https://doi.org/10.54654/isj.v2i17.892

Issue

No 3. CS (17) 2022

Section

Papers

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3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).