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Improved FIFO Scheduling Algorithm Based on fuzzy clustering in Cloud Computing

INFORMATION 2017年第1期8卷 25页

作者： Li, Jian Ma, Tinghuai Tang, Meili Shen, Wenhai Jin, Yuanfeng Nanjing Univ Informat Sci & Technol Sch Comp Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Engn Ctr Network Monitoring CICAEET Nanjing 210044 Jiangsu Peoples R China Natl Meteorol Informat Ctr Beijing 100080 Peoples R China YanBian Univ Dept Math Yanji 133002 Peoples R China

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks' waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks' preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks' waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.

关键词： cloud computing fuzzy clustering algorithms FIFO scheduling algorithm resource clustering

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Grounding Faulted Feeder Detection Based on fuzzy clustering algorithms

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International Conference on Information Technology for Manufacturing Systems (ITMS 2011)

作者： Wang Yuanyuan Jian Jinbao Zeng Xiangjun Long Lihong Guangxi Univ Nanning Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Hunan Peoples R China Hunan Elect Power Design Inst Changsha Hunan Peoples R China

ISBN: (纸本)9783037851494

Many power distribution systems have been operated with neutral ineffectively earthed, and earth fault current is no more than a few tens of amperes. Traditional earth fault detection methods based on zero sequence current has poor precision and sensitivity in this case. For improvement, a novel principle for feeder grounding fault protection based on fuzzy clustering algorithms is presented in this paper. First, the historical data are divided into two groups by fuzzy clustering algorithms. The space relative distance among detected pattern and two cluster centers is then calculated to discriminate the faulted feeder. It can detect high impedance grounding faults (HIGF). The scheme has been verified by EMTP simulation, and results show that the proposed scheme always trips all kinds of grounding faults with high sensitivity and robustness in neutral ineffectively earthed power systems (NIEPS).

关键词： distribution network high impedance grounding faults (HIGF) fault detection fuzzy clustering algorithms neutral ineffectively earthed power systems (NIEPS)

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A heterogeneous cluster ensemble model for improving the stability of fuzzy cluster analysis 12th

A heterogeneous cluster ensemble model for improving the sta...

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12th International Conference on Application of fuzzy Systems and Soft Computing (ICAFS)

作者： Bedalli, Erind Mancellari, Enea Asilkan, Ozcan Epoka Univ Tirana Albania Akdeniz Univ Antalya Turkey

Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves the concept of partial memberships of the instances in the clusters, increasing the flexibility and enhancing the semantics of the generated clusters. Several fuzzy clustering algorithms have been devised like fuzzy c-means (FCM), Gustafson-Kessel, Gath-Geva, kernel-based FCM etc. Although these algorithms do have a myriad of successful applications, each of them has its stability drawbacks related to several factors including the shape and density of clusters, the presence of noise or outliers and the choices about the algorithm's parameters and cluster center initialization. In this paper we are providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis. The key idea of our methodology is the application of different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix. Finally we have experimentally evaluated and compared the accuracy of this methodology. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： fuzzy clustering algorithms heterogeneous fuzzy cluster ensemble consensus matrix

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A Heterogeneous Cluster Ensemble Model for Improving the Stability of fuzzy Cluster Analysis

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Procedia Computer Science 2016年 102卷 129-136页

作者： Erind Bedalli Enea Mançellari Ozcan Asilkan Epoka University Tirana Albania Akdeniz University Antalya Turkey

关键词： fuzzy clustering algorithms heterogeneous fuzzy cluster ensemble consensus matrix

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clustering for Knowledgeable Web Mining

Clustering for Knowledgeable Web Mining

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Charulatha, B. S. Rodrigues, Paul Chitralekha, T. Rajaraman, Arun JNTUK Kakinada India Velammal Engn Coll Madras Tamil Nadu India Cent Univ Puduchery India IIT Madras Madras Tamil Nadu India

ISBN: (纸本)9788132221265;9788132221258

Web pages nowadays have different forms and types of content. When the Web content is considered, they are in the form of pictures, videos, audio files, and text files in different languages. The content can be multilingual, heterogeneous, and unstructured. The mining should be independent of the language and software. Statistical features of the images are extracted from the pixel map of the image. The extracted features are presented to the fuzzy clustering algorithm (FCM) and Gath-Geva algorithm. The similarity metric being Euclidean distance and Gaussian distance, respectively. The accuracy is compared and presented.

关键词： fuzzy clustering algorithms Gath-Geva fuzzy clustering Content mining

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Novel protection scheme of stator single-phase-to-ground fault for powerformers

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 321-328页

作者： Wang, Yuanyuan Zeng, Xiangjun Dong, Zhaoyang Huang, Yue Changsha Univ Sci & Technol Sch Elect & Informat Engn Hunan Prov Key Lab Smart Grids Operat & Control Changsha 410114 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China

This paper presents a novel stator single-phase-to-ground fault protection of Powerformer in parallel based on the fuzzy clustering algorithms. First, the direction and magnitude of zero-sequence current and leakage current are analyzed, and four fault characters are selected as historical data. Then the historical data are divided into two groups by fuzzy clustering algorithms, and cluster center of each group is calculated. The space relative distance among detected pattern and two cluster centers is finally calculated to discriminate the faulty Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish reliably internal faults from external faults, and can detect stator single-phase-to-ground fault occurred in which Powerformer with resistance 5 k Omega. A 100% of the winding can be fully protected. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Powerformer Stator single-phase-to-ground fault fuzzy clustering algorithms

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Estimation of parameters in latent class models using fuzzy clustering algorithms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2005年第2期160卷 515-531页

作者： Yang, MS Yu, NY Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

A mixture approach to clustering is an important technique in cluster analysis. A mixture of multivariate multinormal distributions is usually used to analyze categorical data with latent class model. The parameter estimation is an important step for a mixture distribution. Described here are four approaches to estimating the parameters of a mixture of multivariate multinomial distributions. The first approach is an extended maximum likelihood (ML) method. The second approach is based on the well-known expectation maximization (EM) algorithm. The third approach is the classification maximum likelihood (CML) algorithm. In this paper, we propose a new approach using the so-called fuzzy class model and then create the fuzzy classification maximum likelihood (FCML) approach for categorical data. The accuracy, robustness and effectiveness of these four types of algorithms for estimating the parameters of multivariate binomial mixtures are compared using real empirical data and samples drawn from the multivariate binomial mixtures of two classes. The results show that the proposed FCML algorithm presents better accuracy, robustness and effectiveness. Overall, the FCML algorithm has the superiority over the ML, EM and CML algorithms. Thus, we recommend FCML as another good tool for estimating the parameters of mixture multivariate multinomial models. (C) 2003 Elsevier B.V. All rights reserved.

关键词： categorical data latent class model fuzzy class model maximum likelihood function fuzzy clustering algorithms

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A fuzzy k-partitions model for categorical data and its comparison to the GoM model

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fuzzy SETS AND SYSTEMS 2008年第4期159卷 390-405页

作者： Yang, Miin-Shen Chiang, Yu-Hsuan Chen, Chiu-Chi Lai, Chien-Yo Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

The grade of membership (GoM) model uses fuzzy sets as memberships of each individual to extreme profiles (or classes) on the likelihood function of multivariate multinomial distributions. The GoM clustering algorithm derived from the GoM model is used in cluster analysis for categorical data, but it is iterated with complicated calculations. In this paper we create another approach, termed a fuzzy k-partitions (FkP) model, which is also based on the likelihood function of multivariate multinomial distributions. However, the calculations of the FkP algorithm for clustering categorical data derived from the proposed FkP model are simpler. The proposed FkP clustering algorithm is not only easier in calculation than the GoM, but also has more accuracy and computation efficiency. To verify it, we employ real empirical data and also some simulation data. We find that FkP has superior results to GoM. We then apply these two algorithms to classification of pathology. The results show the superiority of the FkP clustering algorithm. Moreover, the proposed FkP algorithm can be used as a fuzzy clustering algorithm for categorical data. Some comparisons between FkP and two popular algorithms, fuzzy k-modes and fuzzy centroids, are made. These results show that the FkP clustering algorithm can be another useful toot in analyzing categorical data. (c) 2007 Elsevier B.V. All rights reserved.

关键词： categorical data grade of membership (GoM) model fuzzy k-partitions (FkP) model fuzzy clustering algorithms fuzzy k-modes fuzzy centroids

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A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis

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IEEE TRANSACTIONS ON fuzzy SYSTEMS 2007年第5期15卷 904-915页

作者： Yu, Jian Yang, Miin-Shen Beijing Jiaotong Univ Dept Comp Sci Inst Comp Sci Beijing 100044 Peoples R China Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan

In this paper, we propose a generalized fuzzy clustering regularization (GFCR) model and then study its theoretical properties. GFCR unifies several fuzzy clustering algorithms, such as fuzzy c-means (FCM), maximum entropy clustering (MEC), fuzzy clustering based on Fermi-Dirac entropy, and fuzzy bidirectional associative clustering network, etc. The proposed GFCR becomes an alternative model of the generalized FCM (GFCM) that was recently proposed by Yu and Yang. To advance theoretical study, we have the following three considerations. 1) We give an optimality test to monitor if GFCR converges to a local minimum. 2) We relate the GFCR optimality tests to Occam's razor principle, and then analyze the model complexity for fuzzy clustering algorithms. 3) We offer a general theoretical method to evaluate the performance of fuzzy clustering algorithms. Finally, some numerical experiments are used to demonstrate the validity of our theoretical results and complexity analysis.

关键词： fuzzy clustering algorithms fuzzy c-means (FCM) generalized fuzzy clustering regularization (GFCR) model optimality test occam's razor principle model complexity parameter selection

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Cluster-based fuzzy models for groundwater flow in the unsaturated zone

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ADVANCES IN WATER RESOURCES 2007年第4期30卷 701-714页

作者： Vernieuwe, H. Verhoest, N. E. C. De Baets, B. Hoeben, R. De Troch, F. P. Univ Ghent Lab Hydrol & Water Management B-9000 Ghent Belgium Univ Ghent Dept Appl Math Biometr & Proc Control B-9000 Ghent Belgium

In this paper fuzzy models are used as an alternative to describe groundwater flow in the unsaturated zone. The core of these models consists of a fuzzy rule-based model of the Takagi-Sugeno type. Various fuzzy clustering algorithms are compared in the data-driven identification of these Takagi-Sugeno models. The performance of the resulting fuzzy models is evaluated on the training surface on which they were identified, and on time series measurements of water content values obtained through an experiment carried out by the non-vegetated terrain (NVT) workgroup of the European Microwave Signature Laboratory (EMSL) (see [Mancini M, Hoeben R, Troch PA. Multifrequency radar observations of bare surface soil moisture content: a laboratory experiment. Water Resour Res 1999;35(6):1827-38] and [Hoeben R, Troch PA. Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res 2000;36(10):2805-19]). Despite higher errors at the borders of high water content values in the training surface, good results are obtained on the simulation of the time series. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： fuzzy clustering algorithms fuzzy rule-based models Takagi-Sugeno models unsaturated groundwater flow

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