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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Monire Norouzi Shahin Akbarpour Department of Computer Engineering Shabestar BranchIslamic Azad UniversityShabestarIran

ISBN: (纸本)9781467349994

one of the important factors in Grid computing is load balancing conditions on data ***,one of the techniques and tools that we can influence on availability and ability of load balancing is cluster *** this paper,we present some methods which we can divide one large network to some sub *** this dividing,we can analyze load balancing conditions on data processing ***,we use KMeans algorithm for clustering the processors for data processing in network and we investigate the conditions of load balancing on this ***,we show that by executing this algorithm the speed of data processing becomes high and the process traffic are divided between the entire sub networks *** these results,cluster computing techniques such as K-Means algorithm are one of the effective and essential solutions for grid computing problems and load balancing.

关键词： Grid computing Load balancing Data processing cluster algorithms K-Means algorithm

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A cluster controller for transition matrix calculations

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2019年第4期30卷

作者： Yevick, David Lee, Yong Hwan Univ Waterloo Dept Phys Waterloo ON N2L 3G1 Canada

We demonstrate that a temperature schedule for single-spin flip transition matrix calculations can be simply and rapidly generated by monitoring the average size of the Wolff clusters at a set of discrete temperatures. Optimizing this schedule yields a potentially interesting quantity related to the fractal structure of Ising clusters. We also introduce a technique in which the transition matrix is constructed at a sequence of discrete temperatures at which Wolff cluster reversals are alternated with certain series of single-spin flip steps. The single spin-flip transitions are then employed to construct a single transition matrix.

关键词： Monte-Carlo methods statistical physics transition matrix methods phase transitions cluster algorithms

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clustering by Local Gravitation

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第5期48卷 1383-1396页

作者： Wang, Zhiqiang Yu, Zhiwen Chen, C. L. Philip You, Jane Gu, Tianlong Wong, Hau-San Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 39380288 Guangdong Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.

关键词： cluster algorithms cluster analysis clustering density-based clustering

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A Bisecting K-Medoids clustering Algorithm Based on Cloud Model

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IFAC-PapersOnLine 2018年第11期51卷 308-315页

作者： Sun, D. Fei, H. Li, Q. School of Management Shanghai University Shanghai China

In this study, an advanced K-Medoids clustering algorithm has been developed by using an auto-stopped bisecting scheme and replacing the traditional Euclidean distance by cloud similarity. The proposed algorithm is dedicated to cluster the users of Connected TV according to their behavior involved with connected TVs. According to the experimental results, the solutions obtained by the proposed algorithm are quite encouraging and the clustering results are much more stable than Euclidean-distance-based clustering method. The result shows that the proposed method has achieved good results in terms of cluster accuracy and time. © 2018

关键词： clustering algorithms Behavioral research cluster accuracies cluster algorithms clustering results Connected tv Euclidean distance K medoids K medoids clustering User behaviors

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Data Processing in Grid Systems by Using cluster algorithms

Data Processing in Grid Systems by Using Cluster Algorithms

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4th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Norouzi, Monire Akbarpour, Shahin Islamic Azad Univ Dept Comp Engn Shabestar Branch Shabestar Iran

ISBN: (纸本)9781467350006

one of the important factors in Grid computing is load balancing conditions on data processing. Also, one of the techniques and tools that we can influence on availability and ability of load balancing is cluster computing. In this paper, we present some methods which we can divide one large network to some sub network. By this dividing, we can analyze load balancing conditions on data processing better. So, we use K-Means algorithm for clustering the processors for data processing in network and we investigate the conditions of load balancing on this algorithm. Also, we show that by executing this algorithm the speed of data processing becomes high and the process traffic are divided between the entire sub networks normally. By these results, cluster computing techniques such as K-Means algorithm are one of the effective and essential solutions for grid computing problems and load balancing.

关键词： Grid computing Load balancing Data processing cluster algorithms K-Means algorithm

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Extracting Train Driver's Eye-Gaze Patterns Using Graph clustering

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IFAC-PapersOnLine 2016年第19期49卷 621-626页

作者： Horiguchi, Yukio Suzuki, Takaya Sawaragi, Tetsuo Nakanishi, Hiroaki Takimoto, Tomoharu Department of Mechanical Engineering and Science Kyoto University Kyoto615-8540 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Safety Research Institute West Japan Railway Company Osaka545-0053 Japan

The present paper investigates train drivers’ eye-gaze data to find important features to explain behavioral differences between experienced and inexperienced drivers. The obtained eye-gaze data contain too complex transition structure to find any meaningful patterns that might be common across or differentiate drivers. The Markov cluster Algorithm, which is an unsupervised algorithm for graph clustering, is therefore utilized to divide such a structure into clusters that represent constituent eye-gaze patterns of frequent occurrence. As a result, a common eye-gaze pattern was identified to represent a perception tactic that drivers would repetitively move their gaze ahead soon after looking at other specific areas. Comparing cluster structures extracted with different clustering parameter settings clarified that all of the drivers implement this tactic more or less, but that they are different in that the experienced drivers can consistently follow it while the inexperienced drivers can not. © 2016

关键词： clustering algorithms Driver training Eye movements cluster algorithms Eye gaze Pattern extraction Perceptual skills Train drivings

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An Execution Framework for Grid-clustering Methods

An Execution Framework for Grid-Clustering Methods

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16th Annual International Conference on Computational Science (ICCS)

作者： Schikuta, Erich Fritz, Florian Univ Vienna Fac Comp Sci RG WST Vienna Austria

cluster analysis methods have proven extremely valuable for explorative data analysis and are also fundamental for data mining methods. Goal of cluster analysis is to find correlations of the value space and to separate the data values into a priori unknown set of subgroups based on a similarity metrics. In case of high dimensional data, i.e. data with a large number of describing attributes, clustering can result into a very time consuming task, which often limits the number of observations to be clustered in practice. To overcome this problem, Grid clustering methods have been developed, which do not calculate similarity values between the data value each, but organize the value space surrounding the data values, e.g. by specific data structure indices. In this paper we present a framework which allows to evaluate different data structures for the generation of a multi-dimensional grid structure grouping the data values into blocks. The values are then clustered by a topological neighbor search algorithm on the basis of the block structure. As first data structure to be evaluated we present the BANG file structure and show its feasibility as clustering index. The developed framework is planned to be contributed as package to the WEKA software.

关键词： cluster algorithms Dendrogram Grid-based Methods

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An Execution Framework for Grid-clustering Methods

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Procedia Computer Science 2016年 80卷 2322-2326页

作者： Erich Schikuta Florian Fritz University of Vienna Faculty of Computer Science RG WST Vienna Austria

关键词： cluster algorithms Dendrogram Grid-based Methods

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A Better Personalized Image Searching Algorithm

A Better Personalized Image Searching Algorithm

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Huang He Sheng Guang feng Du Jing jing Lei Xu Zhang Tao School of Electronic ＆ Control Engineering in Chang’an University

Searching engines become the major tool for information retrieval of users with the rapidly increasing Web informationTraditional search engines can’t completely evaluate users’ search aimsIt will lead to retrieval quality decline and increased costThe paper proposed a better personalized image searching algorithmIt utilizes relevant feedback and SVM to build user interest model and return the personalized searching results to the users based on the muli-kernel cluster for imagesThe analysis of experiment results indicate that compared with the traditional searching algorithm the improved algorithm can enhance participation methods of users and solve the gap problem between low-level vision feature and high-level semanticsIt can increase the mean recall and precision ratio to 8% and 10.5% compared with the traditional searching algorithm based on colorAt the same time it can increase them to 13.2% and 28.56% respectively compared with the traditional one based on texture.

关键词： image cluster algorithms search engines semantics information fusion

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cluster method for analysing surface X-ray diffraction data sets using area detectors

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2014年第1期47卷 207-214页

作者： Leake, Steven J. Reinle-Schmitt, Mathilde L. Kalichava, Irakli Pauli, Stephan A. Willmott, Philip R. Paul Scherrer Inst Swiss Light Source CH-5232 Villigen Switzerland

An automated cluster algorithm is described, applicable to any image where a signal is to be analysed. The algorithm is employed in the context of surface X-ray diffraction data and extended to automate the data reduction process, which at present limits both the lead time to and the reliability of the retrieved structural information. A detailed evaluation of the constraints used to automate surface X-ray diffraction data analysis is provided. To overcome limitations of the algorithm and the experiment itself in certain geometries, the full field of view of area detectors is exploited to obtain orders of magnitude improvements in data collection. The method extends the surface X-ray diffraction technique to new systems and highlights the often archaic approach to the analysis of data collected with a two-dimensional detector.

关键词： cluster algorithms data analysis image processing surface X-ray diffraction area detectors

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