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A Universal Gravitation Based clustering algorithm for Distributed File System

CHINESE JOURNAL OF ELECTRONICS 2012年第1期21卷 27-32页

作者： Liu Shufen Leng Huang Hu Changhong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

When nodes of Distributed file system are extended over wide area network, network communication has a great influence on the node selection of Distributed file system. In this paper, an improved algorithm is proposed to decrease the transport time by reducing the scale of nodes. This algorithm adopts the law of universal gravitation, which gives strategy of node movement. Meanwhile, to overcome premature or local-best solution, the theory of overcoming premature is referred, and then node can depart for a more suitable cluster. Theoretical proof shows the algorithm converges and has the top limit in the time complexity. Furthermore, experiment results give the availability and efficiency of the algorithm.

关键词： Universal gravity clustering algorithm Node selection Distributed file system

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ESC: An efficient synchronization-based clustering algorithm

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KNOWLEDGE-BASED SYSTEMS 2013年 40卷 111-122页

作者： Huang, Jianbin Sun, Heli Kang, Jianmei Qi, Junjie Deng, Hongbo Song, Qinbao Xidian Univ Sch Software Xian 710071 Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian 710049 Peoples R China Univ Illinois Dept Comp Sci Urbana IL 61801 USA

clustering is an essential approach for detecting the intrinsic groups in data. An efficient clustering algorithm based on a generalized local synchronization model is proposed. It uses a novel stopping criterion of data synchronization to detect clusters prior to the perfect synchronization. Moreover, a density-biased sampling method is adopted to extract samples from the original data set. The clustering structure can be effectively revealed on the samples. As a result, the clustering efficiency is significantly improved. By using a cluster validity criterion, the proposed algorithm can find clusters of arbitrary number, shape, size and density as well as isolate noises in the vector data without any data distribution assumption. Extensive experiments on several synthetic and real-world data sets show that the proposed algorithm possesses high accuracy and it is more efficient than the state-of-the-art synchronization-based clustering method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Dynamical synchronization model Neighborhood closure Density-biased sampling Cluster validity criterion

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Consensus model based on probability K-means clustering algorithm for large scale group decision making

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第6期12卷 1609-1626页

作者： Liu, Qian Wu, Hangyao Xu, Zeshui Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore, large scale group decision making has attracted a lot of researchers to investigate. Since the probabilistic linguistic terms have impressive performance in expressing DMs' opinions, this paper proposes a novel method for large scale group decision making with probabilistic linguistic preference relations. More specifically, (1) a probability k-means clustering algorithm is introduced to segment DMs with similar features into different sub-groups;(2) an integration method is proposed to construct the collective probabilistic preference relation that retains initial information to the most extent;(3) taking the personality of each DM into account, a consensus model is constructed to improve the rationality and efficiency of consensus reaching process. Several simulation experiments are designed to analyze the influence factor in the feedback mechanism and make some comparative analysis with the existing method. Finally, an illustrative example of contractor selection is conducted to verify the validity of the proposed method.

关键词： Large scale group decision making clustering algorithm Consensus reaching process Feedback mechanism Probabilistic linguistic preference relation

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Energy distribution-aware clustering algorithm for dense wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2010年第9-10期23卷 1223-1251页

作者： Fang, Shudong Berber, Stevan Mirko Swain, Akshya Kumar Univ Auckland Dept Elect & Comp Engn Auckland 1 New Zealand

We consider the challenge of organizing densely deployed sensor nodes into the form of clusters, using the distribution of network residual energy (NRE), which is defined as the sum of node residual energy. Irrespective of network topology, the distribution of NRE is proven to approach Gaussian in dense node deployment. A decentralized clustering algorithm is present, using timers and a recursively updated probability to select nodes with more residual energy to become Cluster Head (CH) nodes and organize other nodes in the form of clusters over slotted time intervals. Embracing the dense node deployment, each node initializes its probability of becoming a CH node using the distribution of NRE defined in its neighborhood area. Each of the selected CH nodes resides in the center of its cluster area, which has a radius that can be arbitrarily chosen. The performances of the new clustering algorithm are analyzed and then validated via extensive simulations, taking into account variable cluster radius and variable network density. The new clustering algorithm significantly prolongs the network lifetime, in comparison to several representative and competing clustering algorithms reported in the literature. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： clustering algorithm energy distribution wireless sensor networks

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Distributed Dynamic clustering algorithm for Formation of Heterogeneous Virtual Power Plants Based on Power Requirements

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IEEE TRANSACTIONS ON SMART GRID 2021年第1期12卷 192-204页

作者： Zhang, Runfan Hredzak, Branislav Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Real-time distributed clustering algorithm for aggregation of distributed energy storage systems into heterogeneous virtual power plants is proposed. Two types of virtual power plants are formed: one for provisioning the bulk (low-frequency) power demand and one for provisioning the high-frequency power demand. The proposed distributed clustering algorithm determines the virtual power plants' memberships, for each distributed energy storage system, based on each energy storage system's capacity and owner's willingness to participate in one of the virtual power plants. The proposed distributed secondary level control system regulates each energy storage system according to each virtual power plant's operational objectives. Specifically, a balanced state of charge of all energy storage systems inside each virtual power plant is maintained. One of the virtual power plants is responsible for the frequency and voltage regulation by providing the required high-frequency power, while the other one provides the required bulk (low-frequency) power demand. In addition, the proposed clustering algorithm enables to meet a required energy capacity of the bulk virtual power plant by automatically tuning the clustering algorithm parameters. RTDS real-time technique verifies the proposed clustering algorithm and control systems on the IEEE 13 nodes power system with distributed energy storage systems and photovoltaic sources. The presented results demonstrate dynamic aggregation of energy storage systems into heterogeneous virtual power plants based on power demand while all regulation requirements are met.

关键词： clustering algorithms Power generation Energy storage Power demand Heuristic algorithms Communication networks Control systems Heterogeneous virtual power plants energy storage photovoltaic real-time distributed communication network clustering algorithm frequency regulation voltage restoration bulk power supply state of charge balancing

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Maximum-entropy clustering algorithm and its global convergence analysis

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Science China(Technological Sciences) 2001年第1期44卷 89-101页

作者：张志华郑南宁史罡 1. Institute of Artificial Intelligence & Robotics Xi’ an Jiaotong University 710049 Xi’an China

Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

关键词： clustering algorithm maximum-entropy principle optimization method entropy function convergence

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Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm

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SOFT COMPUTING 2020年第10期24卷 7459-7468页

作者： Kandasamy, Ilanthenral Kandasamy, W. B. Vasantha Obbineni, Jagan M. Smarandache, Florentin Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Vellore Inst Technol VIT Sch Agr Innovat & Adv Learning VAIAL Vellore Tamil Nadu India Univ New Mexico Dept Math 705 Gurley Ave Gallup NM 87301 USA

Likert scale is the most widely used psychometric scale for obtaining feedback. The major disadvantage of Likert scale is information distortion and information loss problem that arise due to its ordinal nature and closed format. Real-world responses are mostly inconsistent, imprecise and indeterminate depending on the customers' emotions. To capture the responses realistically, the concept of neutrosophy (study of neutralities and indeterminacy) is used. Indeterminate Likert scale based on neutrosophy is introduced in this paper. clustering according to customer feedback is an effective way of classifying customers and targeting them accordingly. clustering algorithm for feedback obtained using indeterminate Likert scaling is proposed in this paper. While dealing real-world scenarios, indeterminate Likert scaling is better in capturing the responses accurately.

关键词： Likert scale Star rating Questionnaire Survey Customer feedback Neutrosophy Neutrosophic logic Indeterminacy Indeterminate Likert scale Triple refined neutrosophic set Distance measures Minimum spanning tree clustering algorithm

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A novel clustering algorithm based on mathematical morphology for wind power generation prediction

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RENEWABLE ENERGY 2019年 136卷 572-585页

作者： Hao, Ying Dong, Lei Liao, Xiaozhong Liang, Jun Wang, Lijie Wang, Bo Beijing Inst Technol Dept Automat Beijing Peoples R China Cardiff Univ Sch Engn Cardiff S Glam Wales Beijing Informat Sci & Technol Univ Dept Elect Engn Beijing Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing Peoples R China

Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computational complexity during model simulation reduced by choosing the historical days whose numerical weather prediction information is similar to that of the predicted day as training samples. This paper proposes a new prediction model based on a novel dilation and erosion (DE) clustering algorithm for wind power generation. In the proposed model, the days with similar numerical weather prediction (NWP) information to the predicted day are selected via the proposed DE clustering algorithm, which is based on the basic operations in mathematical morphology. And the proposed DE clustering algorithm can cluster automatically without supervision. Case study conducted using data from Yilan wind farm in northeast China indicate that the performance of the new generalized regression neural network (GRNN) prediction model based on the proposed DE clustering algorithm (DE clustering-GRNN) is better than that of the DPK-medoids clustering-GRNN, the K-means clustering-GRNN, and the AM-GRNN in terms of day-ahead wind power prediction. Further, the proposed DE clustering-GRNN model is adaptive. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Wind power prediction clustering algorithm Dilation and erosion Mathematical morphology The number of clusters

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Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2017年第4期94卷 663-675页

作者： Zhang, Tengfei Ma, Fumin Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing Jiangsu Peoples R China

Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.

关键词： clustering algorithm rough k-means weighted distance measure rough set theory Gaussian function

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DBCURE-MR: An efficient density-based clustering algorithm for large data using MapReduce

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INFORMATION SYSTEMS 2014年第Jun.期42卷 15-35页

作者： Kim, Younghoon Shim, Kyuseok Kim, Min-Soeng Lee, June Sup Seoul Natl Univ Sch Elect & Comp Engn Seoul 151600 South Korea SK Planet Seoul South Korea

clustering is a useful data mining technique which groups data points such that the points within a single group have similar characteristics, while the points in different groups are dissimilar. Density-based clustering algorithms such as DBSCAN and OPTICS are one kind of widely used clustering algorithms. As there is an increasing trend of applications to deal with vast amounts of data, clustering such big data is a challenging problem. Recently, parallelizing clustering algorithms on a large cluster of commodity machines using the MapReduce framework have received a lot of attention. In this paper, we first propose the new density-based clustering algorithm, called DBCURE, which is robust to find clusters with varying densities and suitable for parallelizing the algorithm with MapReduce. We next develop DBCURE-MR, which is a parallelized DBCURE using MapReduce. While traditional density-based algorithms find each cluster one by one, our DBCURE-MR finds several clusters together in parallel. We prove that both DBCURE and DBCURE-MR find the clusters correctly based on the definition of density-based clusters. Our experimental results with various data sets confirm that DBCURE-MR finds clusters efficiently without being sensitive to the clusters with varying densities and scales up well with the MapReduce framework. (C) 2013 Published by Elsevier Ltd.

关键词： clustering algorithm Density-based clustering Parallel algorithm MapReduce

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