检索结果-内蒙古大学图书馆

9th International FLINS conference on computational Intelligence: Foundations and Applications

作者： Kong, Ming Pei, Zheng chang, Ruiqin Luo, Bing Xihua Univ Sch Math & Comp Engn Chengdu 610039 Sichuan Peoples R China

ISBN: (纸本)9789814324694

clustering is an important unsupervised learning technique to discover the inherent structure of a given data set. In this paper, we propose a novel method to determine optimal classes and select optimal samples in data sets, the novel method is based on fuzzy c-means algorithm and the k-means algorithm. An illustrate example shows that our method is simple and valid for clustering and pattern recognition.

关键词： fuzzy c-means algorithm k-means algorithm

来源：评论

学校读者我要写书评

暂无评论

Brain Lesion Detection in MRI with fuzzy and Geostatistical Models

Brain Lesion Detection in MRI with Fuzzy and Geostatistical ...

引用

32nd Annual International conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBc 10)

作者： Pham, Tuan D. Univ New S Wales Sch Informat Technol & Engn Canberra ACT 2600 Australia

ISBN: (纸本)9781424441242

Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy.. means algorithm and geostatistics.

关键词： MRI biomedical MRI brain brain lesion detection clustering-based segmentation cognition cognition elderly fuzzy c-means algorithm fuzzy set theory geostatistical models geriatrics image segmentation magnetic resonance imaging medical image processing statistical analysis white matter lesions

来源：评论

学校读者我要写书评

暂无评论

An Evolutionary clustering algorithm Based on Adaptive fuzzy Weighted Sum Validity Function

An Evolutionary Clustering Algorithm Based on Adaptive Fuzzy...

引用

The Third International Joint conference on computational Science and Optimization(第三届计算科学与优化国际大会 cSO 2010)

作者： Hongbin Dong Wei Hou Guisheng Yin School of Computer Science and Technology Harbin Engineering University Harbin 150001 China

In this paper, we propose a novel objective function called the adaptive fuzzy Weighted Sum Validity Function (FWSVF), which is a merged weight of the several fuzzy cluster validity functions, including XB, PE, Pc and PBMF. The improved validity function is more efficient than others. Furthermore, we present a Mixed Strategy Evolutionary clustering algorithm based adaptive validity function (AMSEcA), which is merged from Evolutionary algorithm along with Mixed Strategy and fuzzy c-means algorithm. Moreover, in the experiments, we show the effectiveness of AMSEcA, AMSEcA could find the proper number of clusters automatically as well as appropriate partitions of the data set and avoid local optima.

关键词： fuzzy c-means algorithm evolutionary algorithm the adaptive fuzzy weighted sum validity function evolutionary clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

The Study of an Improved FcM clustering algorithm

The Study of an Improved FCM Clustering Algorithm

引用

2010 2nd International conference on Signal Processing System(2010年信号处理系统国际会议 IcSPS 2010)

作者： Wang Zebing cui Baozhen The School of Mechanical Engineering and Automation North University of China TaiyuanChina

There are two problems for clustering algorithm of classic fuzzy c-means (FcM). First, the algoritbm of FcM often obtains different clustering results with the different initial cluster centers because it is over-dependent on the initial cluster centers. Second, the algorithm needs to know the actual number of clusters in advance, but in fact the number of clusters is unknown. Tbis paper proposes a solution that we determine a reasonable number and centers of clusters using a weighted Euclidean clustering method, and then use the classical FcM algorithm. It can be significantly reduced the number of algorithm iterations. This method was proved feasibility and effectiveness through the emulation experiment.

关键词： weighted euclidean clustering fcm fuzzy c-means algorithm the number of algorithm iterations

来源：评论

学校读者我要写书评

暂无评论

ANALYSIS OF SESSION SEQUENcES OF STABLE PEERS IN P2P SYSTEMS

ANALYSIS OF SESSION SEQUENCES OF STABLE PEERS IN P2P SYSTEMS

引用

2010 3rd IEEE International conference on Broadband Network & Multimedia Technology(2010年第三届IEEE宽带网络与多媒体国际会议 Ic-BNMT 2010)

作者： Yuxiang Zhang chunjing Xiao Hongke Zhang National Engineering Lab on Next Generation Internet Interconnection Devices Beijing Jiaotong Univer College of Computer Science Civil Aviation University of China Tianjin China National Engineering Lab on Next Generation Internet Interconnection Devices Beijing Jiaotong Univer

This paper presents a formal definition of stable peers, a novel method to separate stable peers from all peers and an analysis of the session sequences of stable peers in P2P (Peer-to-Peer) systems. This study uses the KAD, a P2P file sharing system with several million simultaneous users, as an example and draws some significant conclusions: (1) large numbers of peers with very short session time usually possess few sessions;(2) the stable peers is about 0.6％ of all peers;(3) the 70％ of stable peers possess very long total session time ensured by a large number of sessions, and possess large difference between session time;(4) the 30％ of stable peers, whose average session time is 1.8 times of the former, possess long total session time, a small number of sessions and high availability. We believe that these two types of stable peers can be used for different functions to solve the churn problem in the hierarchical P2P systems.

关键词： approximate entropy per symbol classification tree fuzzy c-means algorithm

来源：评论

学校读者我要写书评

暂无评论

A fuzzy clustering algorithm based on evolutionary programming

引用

EXPERT SYSTEMS WITH APPLIcATIONS 2009年第9期36卷 11792-11800页

作者： Dong, Hongbin Dong, Yuxin Zhou, cheng Yin, Guisheng Hou, Wei Harbin Engn Univ Harbin 150001 Peoples R China Harbin Normal Univ Dept Comp Sci Harbin 150080 Peoples R China

In this paper, a fuzzy clustering method based on evolutionary programming (EPFcM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (FcM). The cluster validity can be measured by some cluster validity indices. To increase the convergence speed of the algorithm, we exploit the modified algorithm to change the number of cluster centers dynamically. Experiments demonstrate EPFcM can find the proper number of clusters, and the result of clustering does not depend critically on the choice of the initial cluster centers. The probability of trapping into the local optima will be very lower than FcM. (c) 2009 Elsevier Ltd. All rights reserved.

关键词： fuzzy c-means algorithm Evolutionary programming cluster validity EPFcM

来源：评论

学校读者我要写书评

暂无评论

New inter-cluster proximity index for fuzzy c-means clustering

引用

IEIcE TRANSAcTIONS ON INFORMATION AND SYSTEMS 2008年第2期E91D卷 363-366页

作者： Li, Fan Dai, Shijin Liu, Qihe Yang, Guowei Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610051 Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 610051 Peoples R China

This letter presents a new inter-cluster proximity index for fuzzy partitions obtained from the fuzzy c-means algorithm. It is defined as the average proximity of all possible pairs of clusters. The proximity of each pair of clusters is determined by the overlap and the separation of the two clusters. The former is quantified by using concepts of fuzzy Rough sets theory and the latter by computing the distance between cluster centroids. Experimental results indicate the efficiency of the proposed index.

关键词： fuzzy c-means algorithm fuzzy rough sets inter-cluster proximity cluster validity fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Study of traffic-emitted lead pollution of soil and plants using different fuzzy clustering algorithms

引用

ANALYTIcAL AND BIOANALYTIcAL cHEMISTRY 2008年第5期390卷 1293-1301页

作者： Sarbu, costel Einax, Juergen W. Univ Babes Bolyai Fac Chem & Chem Engn Dept Analyt Chem Cluj Napoca 400028 Romania Univ Jena Inst Inorgan & Analyt Chem Dept Environm Anal D-07743 Jena Germany

We discuss the clustering of 234 environmental samples resulting from an extensive monitoring program concerning soil lead content, plant lead content, traffic density, and distance from the road at different sampling locations in former East Germany. considering the structure of data and the unsatisfactory results obtained applying classical clustering and principal component analysis, it appeared evident that fuzzy clustering could be one of the best solutions. In the following order we used different fuzzy clustering algorithms, namely, the fuzzy c-means (FcM) algorithm, the Gustafson-Kessel (GK) algorithm, which may detect clusters of ellipsoidal shapes in data by introducing an adaptive distance norm for each cluster, and the fuzzy c-varieties (FcV) algorithm, which was developed for recognition of r-dimensional linear varieties in high-dimensional data (lines, planes or hyperplanes). fuzzy clustering with convex combination of point prototypes and different multidimensional linear prototypes is also discussed and applied for the first time in analytical chemistry (environmetrics). The results obtained in this study show the advantages of the FcV and GK algorithms over the FcM algorithm. The performance of each algorithm is illustrated by graphs and evaluated by the values of some conventional cluster validity indices. The values of the validity indices are in very good agreement with the quality of the clustering results.

关键词： fuzzy c-means algorithm Gustafson-Kessel algorithm fuzzy c-varieties algorithm clustering validity soil and plant samples traffic-emitted lead pollution

来源：评论

学校读者我要写书评

暂无评论

Hybridization of Particle Swarm Optimization with Unsupervised clustering algorithms for Image Segmentation

引用

INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2008年第3期10卷 217-230页

作者： Liu, Wenping McGrath, Ethan Hung, chih-cheng Kuo, Bor-chen Beijing Forestry Univ Beijing Peoples R China

Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The fuzzy c-means algorithm (FcM) and the Possibilistic c-means algorithm (PIcA) have been widely used. There is also the generalized possibilistic algorithm (GPcA). GPcA was proposed recently and is a general form of the previous algorithms. These clustering algorithms can be trapped to the local optimal solutions. Hence, optimization techniques are often used in conjunction with algorithms to improve the performance. Some of optimization techniques have been inspired by nature such as swarm behavior. Particle Swarm Optimization (PSO) is one such technique. In this paper, PSO heuristics were combined with FcM, PIcA, and GPcA algorithms to improve the overall clustering accuracy of these algorithms. To test the improvement with the PSO, these algorithms were tested on images. The overall effect of adding unique PSO methods was a higher percentage of satisfactory image segmentations.

关键词： fuzzy c-means algorithm Possibilistic clustering algorithms Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

A fuzzy cluster algorithm Based on Mutative Scale chaos Optimization

引用

5th International Symposium on Neural Networks

作者： Li, chaoshun Zhou, Jianzhong Li, Qingqing Xiang, Xiuqiao Huazhong Univ Sci & Technol Coll Hydroelect Digitizat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9783540877332

The traditional fuzzy: c-means (FcM) algorithm has some disadvantages in optimization method, which makes the algorithm liable to fall into local optimum, thus failing to get the optimal clustering results. According to the defect of FcM algorithm, a new fuzzy clustering algorithm based on chaos Optimization (FccO) is proposed in this paper, which combines mutative scale, chaos optimization strategy acid gradient method together. Moreover, a fuzzy cluster validity index (PBMF) is introduced to make the FccO algorithm capable of clustering automatically. Three other fuzzy cluster validity indices, namely XB, Pc and PE, are utilized to compare the performances of FccO, MINI and another algorithm, when applied to artificial and real data sets classification. Experiment results show FccO algorithm is more likely to obtain the global optimum and achieve Better performances on validity indices than other algorithms.

关键词： clustering fuzzy c-means algorithm chaos optimization cluster validity indices

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：