Article

# A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure.

Department of Software Engineering, University of Huelva, 21071, La Rabida (Huelva), Spain; Department of Computer Science and A.I, University of Granada, Spain; Department of Computer Science, University of Oviedo, Oviedo, Spain

Journal of Intelligent and Fuzzy Systems (Impact Factor: 0.94). 01/2002; 12:235-242. Source: DBLP

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**ABSTRACT:**A new algorithm is designed for handling fuzziness while mining large data. A new novel cost function weighted by fuzzy membership, is proposed in the framework of CLARANS. A new scalable approximation to the maximum number of neighbors, explored at each node, is developed; thus reducing the computational time for large data while eliminating the need for user-defined (heuristic) parameters in the existing equation. The goodness of the generated clusters is evaluated in terms of Xie–Beni validity index. Results demonstrate the superiority of the proposed algorithm, over both synthetic and real data sets, in terms of goodness of clustering. It is interesting to note that our algorithm always converges to the globally best values at the optimal number of partitions. Moreover compared to existing fuzzy algorithms, FCLARANS without scanning the whole dataset, searching small number of neighbors, is able to handle the uncertainty due to overlapping nature of the various partitions. This is the main motivation of fuzzification of the algorithm CLARANS.Applied Soft Computing. 04/2013; 13(4):1639–1645. - [Show abstract] [Hide abstract]

**ABSTRACT:**Fuzzy C-Means has been used as a popular fuzzy clustering method due to its simplicity and high speed in clustering large data sets. However, C-Means has two shortcomings: dependency on the initial state and convergence to local optima. In this paper a new algorithm based on simulated annealing and possibilistic noise rejection clustering is proposed to reduce the problem of converging to local minima and dependency on initial states. The comparison of the proposed algorithms and some other algorithms in the literature shows that the algorithms outperforms other algorithms in terms of optimization objective function and is capable of doing clustering in noisy environments more efficiently.01/2011; -
##### Article: A multi-objective fuzzy graph approach for modular formulation considering end-of-life issues

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**ABSTRACT:**Increased awareness of the negative environmental impact caused by electronic waste has driven electronics manufacturers to re-engineer their product design process and include product end-of-life considerations into their design criteria. Design for the Environment (DfE), as a possible solution, lacks an implementation framework. To address this problem, a fuzzy graph based modular product design methodology is developed to implement DfE strategies in product modular formulation considering multiple product life cycle objectives guided by DfE. A fuzzy connected graph approach is used to present the product structure, whereby fuzzy relationship values are determined by applying Analytic Hierarchy Process (AHP) to life cycle environmental objectives along with other functional and production concerns. Based on the fuzzy connected graph, an optimal modular formulation is searched using the graph-based clustering algorithm to identify the best module configuration. An example is provided to illustrate the methodology and the algorithm presented in this paper.International Journal of Production Research 01/2008; 46(14). · 1.46 Impact Factor

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