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Research on the Clustering Analysis and Similarity in Factor Space

Sha-Sha Li1,2,∗, Tie-Jun Cui1,2,3,†, Jian Liu1,2,‡

1 College of Safety Science and Engineering, Liaoning Technical Univ., 123000 Fuxin, China
2 Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, 123000 Fuxin, China
3 Tunnel & Underground Structure Engineering Center of Liaoning, Liaoning Dalian Jiaotong University, 116028 Dalian, China

* Corresponding Authors:∗ email
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Computer Systems Science and Engineering 2018, 33(5), 397-404. https://doi.org/10.32604/csse.2018.33.397

Abstract

In this paper, we study the in uence of multiple domain attributes on the clustering analysis of object based on factor space. The representation method of graphical domain attribute is proposed for the object, which is called attribute circle. An attribute circle can represent infinite domain attributes. The similarity analysis of objects is first based on the concept of attribute circle, and the definition of graphical similarity is transformed into the definition of numerical similarity, and then the clustering analysis method of object set is studied and improved. Considering three kinds of graphical overlap, the analytic solution of similarity is obtained for numerical calculation. The clustering rules: strictly obey the similarity division and dissimilarity division, and refer to fuzzy similarity division. The reliability evaluation semantics of the actual electrical system are listed as the study object set, and the clustering analysis method and its improvement are carried out. The results show that the relation between decision set D and object set U means that the division of U is nonsingular and accurate for D. Although the system reliability is evaluated in different environments, these evaluation semantics are relatively objective, and can support each other. The two methods of similarity calculation have the same conclusion, but the improved method is more accurate and complex.

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Cite This Article

S. Li, T. Cui and J. Liu, "Research on the clustering analysis and similarity in factor space," Computer Systems Science and Engineering, vol. 33, no.5, pp. 397–404, 2018.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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