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Journal of Intelligent & Fuzzy Systems - Volume 25, issue 4

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Price: EUR 315.00
ISSN 1064-1246 (P)
ISSN 1875-8967 (E)

Impact Factor 2024: 1.7

The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.

The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.

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EMU: An expectation maximization based approach for clustering uncertain data

Authors: Qin, Biao | Xia, Yuni | Li, Fang | Ge, Jiaqi

Article Type: Research Article

Abstract: Real world applications as sensor networks and RFID networks usually generate data with uncertainty. Data uncertainty comes from many sources, as measurement errors, limited precision, data aggregation and so on. Classical data mining applications need to be modified and extended for uncertain data; otherwise, their performances might be dramatically downgraded by data uncertainty. In this paper, we define an uncertain data model for both numerical and categorical uncertain data, and propose a new Expectation-Maximization based algorithm (EMU) for clustering uncertain data. This approach is well designed to find the distribution parameters that maximize model qualities based on uncertain data, therefore … Show more

Keywords: Uncertain database, clustering, Expectation-Maximization

DOI: 10.3233/IFS-130794

Citation: Journal of Intelligent & Fuzzy Systems, vol. 25, no. 4, pp. 1067-1083, 2013

Price: EUR 27.50

Author Index Volume 25 (2013)

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 25, no. 4, pp. 1085-1090, 2013

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