clustering for the analysis of the genes organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster and analyzing the functions of u...
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clustering for the analysis of the genes organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster and analyzing the functions of unknown genes. Since the genes usually belong to multiple functional families, fuzzyclustering methods are more appropriate than the conventional hard clustering methods which assign a sample to only one group. In this paper, a Bayesian-like validation method selecting a fuzzy partition is proposed to evaluate the fuzzy partitions effectively. The theoretical interpretation of the obtained memberships is beyond the scope of this paper, and an empirical evaluation of the proposed method is conducted by comparing to the four representative conventional fuzzycluster validity measures in four well-known datasets. Analysis of yeast cell-cycle data follows to evaluate the proposed method. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approac...
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clustering aims to partition a data set into homogenous groups which gather similar objects. Object similarity, or more often object dissimilarity, is usually expressed in terms of some distance function. This approach, however, is not viable when dissimilarity is conceptual rather than metric. In this paper, we propose to extract the dissimilarity relation directly from the available data. To this aim, we train a feedforward neural network with some pairs of points with known dissimilarity. Then, we use the dissimilarity measure generated by the network to guide a new unsupervised fuzzy relational clustering algorithm. An artificial data set and a real data set are used to show how the clustering algorithm based on the neural dissimilarity outperforms some widely used (possibly partially supervised) clustering algorithms based on spatial dissimilarity.
Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been...
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ISBN:
(纸本)0878499903
Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The fuzzyc-means (FcM) algorithm, which is best known an unsupervised fuzzyclustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzyclustering technique, and the optimal bead geometry can also be achieved.
In this paper, we present an efficient implementation of the fuzzyc-meansclustering algorithm. The original algorithm alternates between estimating centers of the clusters and the fuzzy membership of the data points...
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In this paper, we present an efficient implementation of the fuzzyc-meansclustering algorithm. The original algorithm alternates between estimating centers of the clusters and the fuzzy membership of the data points. The size of the membership matrix is on the order of the original data set, a prohibitive size if this technique is to be applied to very large data sets with many clusters. Our implementation eliminates the storage of this data structure by combining the two updates into a single update of the cluster centers. This change significantly affects the asymptotic runtime as the new algorithm is linear with respect to the number of clusters, while the original is quadratic. Elimination of the membership matrix also reduces the overhead associated with repeatedly accessing a large data structure. Empirical evidence is presented to quantify the savings achieved by this new method.
In this article, the effectiveness of variable string length geneticalgorithm along with a recently developed fuzzycluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number...
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In this article, the effectiveness of variable string length geneticalgorithm along with a recently developed fuzzycluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. The flexibility of a variable string length Geneticalgorithm (VGA) is utilized in conjunction with the fuzzy indices to determine the number of clusters present in a data set as well as a good, fuzzy partition of the data for that number of clusters. A comparative study has been performed for different validity indices, namely, PBMF, XB, PE and Pc. The results of the fuzzy VGA algorithm are compared with those obtained by the well known FcM algorithm which is applicable only when the number of clusters is fixed a priori. Moreover, another geneticclustering scheme, that also requires fixing the value of the number of clusters, is implemented. The effectiveness of the PBMF index as the optimization criterion along with a geneticfuzzy partitioning technique is demonstrated on a number of artificial and real data sets including a remote sensing image of the city of Kolkata. (c) 2005 Elsevier B.V. All rights reserved.
The field of cluster analysis is primarily concerned with the partitioning of data points into different clusters so as to optimize a certain criterion. Rapid advances in technology have made it possible to address cl...
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The field of cluster analysis is primarily concerned with the partitioning of data points into different clusters so as to optimize a certain criterion. Rapid advances in technology have made it possible to address clustering problems via optimization theory. In this paper, we present a global optimization algorithm to solve the fuzzyclustering problem, where each data point is to be assigned to (possibly) several clusters, with a membership grade assigned to each data point that reflects the likelihood of the data point belonging to that cluster. The fuzzyclustering problem is formulated as a nonlinear program, for which a tight linear programming relaxation is constructed via the Reformulation-Linearization Technique (RLT) in concert with additional valid inequalities. This construct is embedded within a specialized branch-and-bound (B&B) algorithm to solve the problem to global optimality. computational experience is reported using several standard data sets from the literature as well as using synthetically generated larger problem instances. The results validate the robustness of the proposed algorithmic procedure and exhibit its dominance over the popular fuzzy c-means algorithmic technique and the commercial global optimizer BARON.
These days consumers' various demands are accelerating research on apparel manufacturing system including automatic measurement, pattern generation, and clothing simulation. Accordingly, methods of reconstructing ...
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These days consumers' various demands are accelerating research on apparel manufacturing system including automatic measurement, pattern generation, and clothing simulation. Accordingly, methods of reconstructing human body from point-clouds measured using a three dimensional scanning device are required for apparel cAD system to support these functions. In particular, we present in this study a human body reconstruction method focused on two issues, which are the decision of the number of control point for each sectional curve with error bound and the local knot removal for reducing the unusual concentration of control points. The approximation of sectional curves with error bounds as an approximation criterion leads all sectional curves to their own particular shapes apart from the number of control points. In addition, the application of the local knot removal to construction of human body sectional curves reduces the unusual concentration of control points effectively. The results may be used to produce an apparel cAD system as an automatic pattern generation system and a clothing simulation system through the low level control of NUBS or NURBS.
Trust is an agent's expectation of other agent's capability, which needs to be confirmed by experience of various peer agents in application domain about capability of agent. Majority view of peer agents mater...
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ISBN:
(纸本)1932415793
Trust is an agent's expectation of other agent's capability, which needs to be confirmed by experience of various peer agents in application domain about capability of agent. Majority view of peer agents materializes into reputation of agent. In our model trustier agent computes reputation based on its own experience as well as experience peer agents have with trustee agent. The trustier agent interacts with peer agents to get their experience information inform of recommendations. The concept of reputation is subjective and Intuitionisticfuzzy Sets (IFS) are used in this paper to model reputation. fuzzy hierarchical agglomerative clustering is done to filter off the noise in IFS recommendations in form of outliers by cutting dendogram at required similarity level. The cluster with maximum number of elements denotes views of majority of recommenders and its center represents reputation Of trustee agent, which is computed using fuzzy c-means algorithm.
Using historical time-series data, we test for convergence and common trends in real per capita output for New Zealand and her four major trading partners. Both bivariate and multivariate time-series methods are used,...
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In this article, a cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set. The maximum value of this index, called the PB...
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In this article, a cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set. The maximum value of this index, called the PBM-index, across the hierarchy provides the best partitioning. The index is defined as a product of three factors, maximization of which ensures the formation of a small number of compact clusters with large separation between at least two clusters. We have used both the k-means and the expectation maximization algorithms as underlying crisp clustering techniques. For fuzzyclustering, we have utilized the well-known fuzzy c-means algorithm. Results demonstrating the superiority of the PBM-index in appropriately determining the number of clusters, as compared to three other well-known measures, the Davies-Bouldin index, Dunn's index and the Xie-Beni index, are provided for several artificial and real-life data sets. (c) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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