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Mathematical Problems in Engineering
Volume 2015, Article ID 495829, 11 pages
http://dx.doi.org/10.1155/2015/495829
Research Article

Fuzzy Collaborative Clustering-Based Ranking Approach for Complex Objects

1School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China
2Key Laboratory of IOT Application Technology of Universities in Yunnan Province, Yunnan Minzu University, Kunming 650031, China
3Department of Mathematics and Statistics, Allama Iqbal Open University, Islamabad 44000, Pakistan
4School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China

Received 14 March 2015; Revised 4 July 2015; Accepted 6 July 2015

Academic Editor: Laura Gardini

Copyright © 2015 Shihu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper makes a discussion on the ranking problem of complex objects where each object is composed of some patterns described by individual attribute information as well as the relational information between patterns. This paper presents a fuzzy collaborative clustering-based ranking approach for this kind of ranking problem. In this approach, a referential object is employed to guide the ranking process. To achieve the final ranking result, fuzzy collaborative clustering is carried on the patterns in the referential object by using the collaborative information obtained from each ranked object. Since the collaborative information of ranking objects is represented by cluster centers and/or partition matrices, we give two forms of the proposed approach. With the aid of fuzzy collaborative clustering, the ranking results can be obtained by comparing the difference of the referential object before and after collaboration with respect to ranking objects. One can find that this proposed ranking approach is totally different from the previous ranking methods because of its completely collaborative clustering mechanism. Moreover, some synthetic examples show that our proposed ranking algorithm is valid.