Fuzzy C-Means is one of the clustering algorithms based on optimizing an objective *** selection of the initial parameters of the number and the initial cluster centers play an important influence in the performance o...
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Fuzzy C-Means is one of the clustering algorithms based on optimizing an objective *** selection of the initial parameters of the number and the initial cluster centers play an important influence in the performance of the *** paper proposes a new FCM clustering algorithm with two *** proposed algorithm not only resolves the problem of the initial choice of the cluster center effectively,but also decreases the time when clustering large volume of *** computer simulation results show the effectivity and the superiority of the new algorithm.
clustering analysis is an important function of data *** clustering methods are need for different domains and applications.A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is prop...
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clustering analysis is an important function of data *** clustering methods are need for different domains and applications.A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is proposed in this ***-Cluster algorithm introduces the concept of multi-population of ants with different speed, and adopts fixed moving times method to deal with outliers and locked ant ***, we experiment on a telecom company's customer data set with SWARM, agent-based model simulation software, which is integrated in SIMiner, a data mining software system developed by our own studies based on swarm *** results illuminate that Ant-Cluster algorithm can get clustering results effectively without giving the number of clusters and have better performance than k-means algorithm.
Along with information on the Internet increasing dramatically, People usually search and locate information that they needed by search engines. clustering search engine results is an effective method to help people s...
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Along with information on the Internet increasing dramatically, People usually search and locate information that they needed by search engines. clustering search engine results is an effective method to help people select information needed from the list of search engine results. The paper presents a clustering algorithm of no-word-segmentation for Chinese search engine results (CANWS). The algorithm firstly preprocesses the search engine results and then computes the similarities of the results based on the same sub-string. Lastly it clusters the results based on the similarity matrix. The paper also gives test and analysis of the algorithm performance by experiments.
By means of calculating density threshold data,some effective referential values of parameters were worked out and provided for users,and a new kind of clustering algorithm called GRPC was *** the help of these refere...
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By means of calculating density threshold data,some effective referential values of parameters were worked out and provided for users,and a new kind of clustering algorithm called GRPC was *** the help of these referential values of parameters,we can not only cluster general data but also segregate high-density clusters from low-density *** problem of lower quality of clusters in traditional grid clustering algorithm which usually ignored the distribution of data when meshing was *** results indicated that this new algorithm can differentiate between outliers or noises and clusters effectively and treat clusters of arbitrary *** new algorithm also displayed a good clustering quality.
A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. This approach performs well on function curves and character image...
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A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. This approach performs well on function curves and character images that not only have loops, sharp corners and intersections but also include data with noise and outliers, The proposed approach is composed of two phases: firstly, input data are clustered using the proposed distance analysis to get good and reasonable number of Clusters: secondly, the input data are further re-clustered by the proposed robust fuzzy C-means (RFCM) to mitigate the influence of noise and outlier data so that a good result of principal components can be found. Experimental results have shown the approach works well on PCA for both curves and images despite their input data sets include loops, corners, intersections, noise and outliers.
clustering is a widely used unsupervised learning method to group data with similar characteristics. The performance of the clustering method can be in general evaluated through some validity indices. However, most va...
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ISBN:
(数字)9783642162480
ISBN:
(纸本)9783642162473
clustering is a widely used unsupervised learning method to group data with similar characteristics. The performance of the clustering method can be in general evaluated through some validity indices. However, most validity indices are designed for the specific algorithms along with specific structure of data space. Moreover, these indices consist of a few within-and between-clustering distance functions. The applicability of these indices heavily relies on the correctness of combining these functions. In this research, we first summarize three common characteristics of any clustering evaluation: (1) the clustering outcome can be evaluated by a group of validity indices if some efficient validity indices are available, (2) the clustering outcome can be measured by an independent intra-cluster distance function and (3) the clustering outcome can be measured by the neighborhood based functions. Considering the complementary and unstable natures among the clustering evaluation, we then apply Dampster-Shafter (D-S) Evidence Theory to fuse the three characteristics to generate a new index, termed fused Multiple Characteristic Indices (fMCI). The fMCI generally is capable to evaluate clustering outcomes of arbitrary clustering methods associated with more complex structures of data space. We conduct a number of experiments to demonstrate that the fMCI is applicable to evaluate different clustering algorithms on different datasets and the fMCI can achieve more accurate and robust clustering evaluation comparing to existing indices.
A calculus of appropriateness measures of linguistic expressions is proposed, which is based on the prototype theory and random set theory interpretation of vague concepts. A prototype-based rule inference system is t...
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A calculus of appropriateness measures of linguistic expressions is proposed, which is based on the prototype theory and random set theory interpretation of vague concepts. A prototype-based rule inference system is then introduced to incorporate linguistic labels in the rule antecedents and linear functions in the consequents of rules. And a rule learning algorithm is developed by combining a new clustering algorithm and a conjugate gradient algorithm. The proposed prototype-based inference system is then applied to a number of benchmark prediction problems including a nonlinear two-dimensional surface, the Mackey-Glass time series and the sunspot time-series. Results suggest that the proposed model is very robust and can perform well in high-dimensional noisy data. (C) 2010 Elsevier B.V. All rights reserved.
There exists a nonlinear relationship between fertilizer input and soil nutrient level. To calculate the fertilization rate more precisely, a novel neural network ensemble method has been proposed, in which the K-mean...
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There exists a nonlinear relationship between fertilizer input and soil nutrient level. To calculate the fertilization rate more precisely, a novel neural network ensemble method has been proposed, in which the K-means clustering method is used to select optimal networks individually and a Lagrange multiplier is used to combine these selected networks. On the basis of the above neural network ensemble method, a fertilization model is constructed. In this model, the soil nutrient level and the fertilization rate are taken as neural network inputs and the yield is taken as the output. This model transforms the calculation of the fertilization rate into solving a programming problem, and can be used to calculate the fertilization rate with maximum yield and maximum profit as well as to forecast the yield. Furthermore, this fertilization model has been tested on fertilizer effect data. The results show that the value forecast using the neural network ensemble is more accurate than that obtained with individual neural networks. The fertilization model constructed in this paper not only can precisely simulate the nonlinear relationship between yield and soil nutrient level, but also can adequately make use of the existing fertilizer effect data. (C) 2009 Elsevier Ltd. All rights reserved.
Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. Thi...
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Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. This paper proposes a new approach for detecting skin in a single image. This approach uses a local skin model to shift a globally trained skin model to adapt the final skin model to the current image. Experimental results show that the proposed method can achieve better accuracy. Two improvements for speeding up the processing are also discussed. (C) 2009 Elsevier Ltd. All rights reserved.
In this paper, we propose a new architecture based on an efficient trust model and secure distributed clustering algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of ...
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In this paper, we propose a new architecture based on an efficient trust model and secure distributed clustering algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of certificates in each cluster. We use the combination of a fully self-organized security for trust models like pretty good privacy (PGP) adapted to ad hoc technology and the clustering algorithm which is based on the use of trust and mobility metrics, in order to select the clusterhead and to establish a public key infrastructure (PKI) in each cluster for authentication and exchange of data. Furthermore, we present a new approach: the dynamic demilitarized zone (DDMZ) to protect the CA in each cluster. The principal idea of DDMZ consists in selecting the dispensable nodes, also called registration authorities (RAs);these nodes must be confident and located at one-hope from the CA. Their roles are to receive, filter and treat the requests from any unknown node to the A. With this approach, we can avoid the single point of failure in each cluster. Moreover, we propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of the DDMZ against different attacks. In addition, we evaluate the performance of the proposed SDCA and we estimate the robustness and the availability of DDMZ through the simulations. The effects of direct connectivity and transmission range on the stability and security of the network are analyzed. The simulation's results confirm that the proposed architecture is scalable, secure, and more resistant against attacks. Copyright (C) 2009 John Wiley & Sons, Ltd.
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