Chapter
Apr 26, 2012
Target Clustering Based on Integrated Discrepancy-Degree for Mixed-Type Attributes
Authors: Jia Zeng [email protected], Lingyu Yang [email protected], and Gongzhang Shen [email protected]Author Affiliations
Publication: Earth and Space 2010: Engineering, Science, Construction, and Operations in Challenging Environments
Abstract
Target clustering forms the targets with similar attributes into several independent clusters, which is essential for the situation assessment. In this paper, a novel clustering method for the targets with mixed-type attributes is proposed. First of all, the attribute types are given according to the fundamental types of data, two new types, the "Vector-type attribute" and the "Interval-type attribute", are defined. Based on the characteristics of attribute types, the discrepancy-degree for each type is built, which extends the existing calculation method. An integrated discrepancy-degree for mixed-type attributes is established, where the quantitative or qualitative requirement of attribute is considered. The integrated discrepancy-degree solves the problem that some attributes are unsuitable for weighted sum. There is only one comprehensive comparison between individual targets and only one threshold needed to set. Several targets with mixed-attributes are selected into simulations and the results demonstrate the effectiveness of this approach.
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© 2010 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, 100191. E-mail: [email protected]
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, 100191. E-mail: [email protected]
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China, 100191. E-mail: [email protected]
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