Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)

Fuzzy Optimization Multi-objective Clustering Ensemble Model for Multi-source Data Analysis

Authors
Le Thi Cam Binh, Pham Van Nha, Ngo Thanh Long
Corresponding Author
Pham Van Nha
Available Online 30 August 2021.
DOI
https://doi.org/10.2991/asum.k.210827.017How to use a DOI?
Keywords
Clustering ensemble, multi-source, multi-objective, fuzzy clustering
Abstract

In modern data analysis, multi-source data appears more and more in real applications. Different data sources provide information about different data. Therefore, multi-source data linking is important to improve the processing performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a universal machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. In this paper, we propose a new clustering ensemble method for multi-source data analysis. We call the fuzzy optimized multi-objective clustering ensemble method - FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. Experiments were performed on benchmark data sets. The experimental results demonstrate the superior performance of the FOMOCE method compared with the existing clustering ensemble methods and multi-source clustering methods.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Cite this article

TY  - CONF
AU  - Le Thi Cam Binh
AU  - Pham Van Nha
AU  - Ngo Thanh Long
PY  - 2021
DA  - 2021/08/30
TI  - Fuzzy Optimization Multi-objective Clustering Ensemble Model for Multi-source Data Analysis
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 125
EP  - 133
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.017
DO  - https://doi.org/10.2991/asum.k.210827.017
ID  - Binh2021
ER  -