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Liao, Hai-yong; Ng, Michael K.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0001-5
AbstractIn this paper, we investigate the problem of determining the number of clusters in the k-modes based categorical data clustering process. We propose a new categorical data clustering algorithm with automatic selection of k. The new algorithm extends the k-modes clustering algorithm by introducing a penalty term to the objective function to make more clusters compete for objects. In the new objective function, we employ a regularization parameter to control the number of clusters in a clustering process. Instead of finding k directly, we choose a suitable value of regularization parameter such that the corresponding clustering result is the most stable one among all the generated clustering results. Experimental results on synthetic data sets and the real data sets are used to demonstrate the effectiveness of the proposed algorithm.
Ovaere, Koen; Deschrijver, Glad; Kerre, Etienne E.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0002-4
AbstractIn this paper we present a new approach to handle uncertainty in the Finite Element Method. As this technique is widely used to tackle real-life design problems, it is also very prone to parameter-uncertainty. It is hard to make a good decision regarding design optimization if no claim can be made with respect to the outcome of the simulation. We propose an approach that combines several techniques in order to offer a total order on the possible design choices, taking the inherent fuzziness into account. Additionally we propose a more efficient ordering procedure to build a total order on fuzzy numbers.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0003-3
AbstractA rough posynomial geometric programming is put forward by the author. This model is advantageous for us to consider questions not only from the quantity of aspect, but from the quality because it contains more information than a traditional geometric programming one. Here, a rough convex function concept is advanced in rough value sets on foundation of rough sets and rough convex sets. Besides, a knowledge expression model in rough posynomial geometric programming is established and so is a mathematical one. Thirdly, solution properties are studied in mathematical model of rough posynomial geometric programming, and antinomy of the more-for-less paradox is solved with an arithmetic in rough posynomial geometric programming given, which can be changed into a rough linear programming after monomial rough posynomial geometric programming is solved. Finally, validity in model and algorithm is verified by examples.
Nasseri, S.H.; Mahdavi-Amiri, N.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0004-2
AbstractRecently, linear programming problems with symmetric fuzzy numbers (LPSFN) have considered by some authors and have proposed a new method for solving these problems without converting to the classical linear programming problem, where the cost coefficients are symmetric fuzzy numbers (see in [4]). Here we extend their results and first prove the optimality theorem and then define the dual problem of LPSFN problem. Furthermore, we give some duality results as a natural extensions of duality results for linear programming problems with crisp data.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0005-1
AbstractThe agent's private information contributes greatly to a person to make principal decision in the supply of a chain coordination. Therefore, it is a great issue for him to design an effective incentive mechanism in order to get the true information from the agent in his principle making. Assuming that the demand depend upon an agent's effort level and the fuzzy market condition, the author in this paper researches and analyzes the principle-agent problem under fuzzy information asymmetry condition by using the theory of principal-agent as well as incentive mechanism.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0006-0
AbstractAn intuitionistic preference relation is a powerful means to express decision makers' information of intuitionistic preference over criteria in the process of multi-criteria decision making. In this paper, we first define the concept of its consistence and give the equivalent interval fuzzy preference relation of it. Then we develop a method for estimating criteria weights from it, and then extend the method to accommodate group decision making based on them And finally, we use some numerical examples to illustrate the feasibility and validity of the developed method.
Zheng, Ya-lin; Cao, Bing-yuan; Yang, Guang; Bai, Yong-cheng
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0007-z
AbstractIt can reflect the nature of approximate reasoning and meet more application expectations to design the approximate reasoning matching schemes and the corresponding algorithms with similarity relation Q instead of equivalence relation R. In this paper, based on similarity relation Q, we introduce type V matching scheme and corresponding approximate reasoning type V Q-algorithm with the given input A* and knowledge A → B. Besides, we present completeness of type V and its perfection on knowledge base K in Q-logic ℂQ in this paper.
2009 Fuzzy Information and Engineering
doi: 10.1007/s12543-009-0008-y
AbstractSelecting or ranking available alternatives (observations/objects) with respect to multiple, often conflicting criteria in a fuzzy environment usually referred to as fuzzy multicriteria analysis is a problem of a major interest in information and engineering. Methodologies for addressing this problem have been developed from a variety of research disciplines, including statistics, econometrics, artificial intelligent, and operations research. This paper presents an overview of the developments in fuzzy multicriteria analysis. It discusses the complexity of fuzzy multicriteria analysis and analyses the existing approaches from four different perspectives for facilitating a better understanding of the recent development in this domain. Finally, the paper elaborates on the future research areas in fuzzy multicriteria analysis.
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