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Resource clustering algorithm for cloud data centers

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1575-1585页

作者： Song, Caili Liang, Bin Li, Jiao Xian Shiyou Univ Comp Sch Xian 710065 Shaanxi Peoples R China

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider's earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine's use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine's use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine's use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.

关键词： Cloud data centers clustering algorithm Machine learning Virtual machine deployment

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A NOVEL clustering algorithm BASED ON P SYSTEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年第2期10卷 753-765页

作者： Jiang, Yang Peng, Hong Huang, Xiaoli Zhang, Jiarong Shi, Peng Xihua Univ Ctr Radio Adm & Technol Dev Chengdu 610039 Sichuan Peoples R China Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China Victoria Univ Sch Engn & Sci Melbourne Vic 8001 Australia

Membrane computing (known as P systems) is a novel class of distributed parallel computing models. In this paper, a partition-based clustering algorithm under the framework of membrane computing is proposed. The clustering algorithm is based on a tissue-like P system, which is used to exploit the optimal cluster centers for a data set. Each object in the tissue-like P system represents a group of candidate cluster centers and is evolved through simulated annealing mechanism and mutation mechanism. Meanwhile, communication rules are used to exchange and share the objects between different elementary membranes and between elementary membranes and the environment. The proposed clustering algorithm is evaluated over two artificial data sets and two real-life data sets and is further compared with k-means algorithm and GA-based k-means algorithm respectively. The comparison results reveal the superiority of the proposed clustering algorithm in terms of clustering quality and stability.

关键词： Membrane computing Tissue-like P systems clustering algorithm Simulated annealing K-means

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Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

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JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 1014-1025页

作者： Chen, Zhuoran Cong, Biao Hua, Zhenxing Cengiz, Korhan Shabaz, Mohammad Jilin Normal Univ Boda Coll Sch Comp & Informat Sci Siping City Peoples R China Jilin Normal Univ Inst Comp Sci Siping City Peoples R China Trakya Univ Dept Elect Elect Engn Edirne Turkey Arba Minch Univ Arba Minch Ethiopia

In synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.

关键词： clustering algorithm farmland SAR image segmentation regional algorithms noise interference

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Massive Data Mining algorithm for Web Text Based on clustering algorithm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第2期23卷 362-365页

作者： Luo, Nan-Chao Aba Teachers Univ Sch Math & Comp Sci Wenchuan 623002 Sichuan Peoples R China

The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of Web text by using the current data mining algorithms. To solve these problems, a massive data mining algorithm of Web text based on clustering algorithm is proposed. By using chi square test, the feature words of massive data are extracted and the set of characteristic words is gotten. Hierarchical clustering of feature sets is made, TF-IDF values of each word in clustering set are calculated, and vector space model is constructed. By introducing fair operation and clone operation on bee colony algorithm, the diversity of vector space models can be improved. For the result of the clustering center, K-means is introduced to extract the local centroid and improve the quality of data mining. Experimental results show that the proposed algorithm can effectively improve data mining accuracy and time consuming.

关键词： clustering algorithm Web text massive data data mining algorithm

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γ-SUP: A clustering algorithm FOR CRYO-ELECTRON MICROSCOPY IMAGES OF ASYMMETRIC PARTICLES

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ANNALS OF APPLIED STATISTICS 2014年第1期8卷 259-285页

作者： Chen, Ting-Li Hsieh, Dai-Ni Hung, Hung Tu, I-Ping Wu, Pei-Shien Wu, Yi-Ming Chang, Wei-Hau Huang, Su-Yun Acad Sinica Inst Stat Sci Taipei 11529 Taiwan Natl Taiwan Univ Inst Epidemiol & Prevent Med Taipei 10055 Taiwan Duke Univ Dept Biostat & Bioinformat Durham NC 27710 USA

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of identical particles photographed with a small number of electrons. The computation of 3D structure from 2D projections requires clustering, which aims to enhance the signal to noise ratio in each view by grouping similarly oriented images. Nevertheless, the prevailing clustering techniques are often compromised by three characteristics of cryo-EM data: high noise content, high dimensionality and large number of clusters. Moreover, since clustering requires registering images of similar orientation into the same pixel coordinates by 2D alignment, it is desired that the clustering algorithm can label misaligned images as outliers. Herein, we introduce a clustering algorithm gamma-SUP to model the data with a q-Gaussian mixture and adopt the minimum gamma-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply gamma-SUP to the cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome. In the former case, simulated images were chosen to decouple clustering from alignment to demonstrate gamma-SUP is more robust to misalignment outliers than the existing clustering methods used in the cryo-EM community. In the latter case, the clustering of real cryo-EM data by our gamma-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.

关键词： clustering algorithm cryo-EM images gamma-divergence k-means mean-shift algorithm multilinear principal component analysis q-Gaussian distribution robust statistics self-updating process

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A novel ICA-based clustering algorithm for heart arrhythmia diagnosis

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PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 285-297页

作者： Naseri, E. Ghaffari, A. Abdollahzade, M. KN Toosi Univ Technol Dept Mech Engn Tehran Iran KN Toosi Univ Technol CardioVasc Res Grp CVRG Tehran Iran

Thousands of people around the world are suffered from heart diseases;however, a considerable amount of them can have a chance of survival if there is an accurate and accessible diagnosis method. This paper introduces a new method for clustering of Holter electrocardiogram QRS complexes based on imperialist competitive optimization algorithm (ICA) which is the main contribution of this paper to raise the accuracy of diagnosis and find the methods for heart disease accessible machine diagnosis. The procedure of clustering is carried out using a mathematical modeling based on defining a cost function which is the ratio between the distance of each pattern's features within each cluster (DWC) and the distance between the clusters. Hence, the clustering problem is reduced to an optimization process. The recently introduced optimization algorithm of ICA, inspired by imperialistic competition, is applied to solve the resulting optimization problem and to find the appropriate weighting factors. To demonstrate the effectiveness of the proposed clustering method, it was implemented on 5 set of MIT records obtained from MIT-BIH Arrhythmia Database records and also on hand-designed datasets (HDD). HDDs developed by selecting and combining some sets of computer-based simulated QRS complexes developed by CVRG group. To compare the effectiveness of the proposed approach, simulated annealing and genetic algorithm were also employed as other optimization algorithms. The results were promising and showed the ability of the proposed method for the clustering applications.

关键词： Feature extraction Curve length method Optimization algorithm Imperialist competitive algorithm clustering algorithm ECG clustering

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Supervised adaptive clustering: A hybrid neural network clustering algorithm

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NEURAL COMPUTING & APPLICATIONS 1998年第1期7卷 78-89页

作者： Augusteijn, MF Steck, UJ Univ Colorado Dept Comp Sci Colorado Springs CO 80933 USA

A neural network architecture is introduced which implements a supervised clustering algorithm for the classification of feature vectors. The network is self-organising, and is able to adapt to the shape of the underlying pattern distribution as well as detect novel input vectors during training. It is also capable of determining the relative importance of the feature components for classification. The architecture is a hybrid of supervised and unsupervised networks, and combines the strengths of three well-known architectures: learning vector quantisation, back-propagation and adaptive resonance theory. Network performance is compared to that of learning vector quantisation, back-propagation and cascade-correlation. it is found that performance is generally as good as or better than the performance of these other architectures, while training time is considerably shorter. However the main advantage of the hybrid architecture is its ability to gain insight into the feature pattern space.

关键词： classification clustering algorithm dynamic architecture hybrid architecture learning vector quantisation supervised learning

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Density Based clustering algorithm for Vehicular Ad-Hoc Networks

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INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2009年第3期4卷 149-157页

作者： Kuklinski, Slawomir Wolny, Grzegorz Warsaw Univ Technol Fac Elect & Informat Technol Inst Telecommun Nowowiejska 15-19 PL-00665 Warsaw Poland Res & Dev Ctr Branch PL-02691 Warsaw Poland Univ Warsaw Fac Math Informat & Mech PL-02097 Warsaw Poland

In this paper we present a new multilevel clustering algorithm for Vehicular Ad-Hoc Networks (VANET), which we call the Density Based clustering (DBC). Our solution is focused on the formation of stable, long living clusters. Cluster formation is based on a complex clustering metric which takes into account the density of the connection graph, the link quality and the road traffic conditions. The tests performed in the simulation environment composed of VanetMobiSim and Java in Simulation Time (JiST)/SWANS have shown that DBC performs better than the popular approach (the Lowest Id algorithm)-the clusters stability has been significantly increased.

关键词： clustering algorithm clustering protocol VANET density stability ad-hoc network reliable communication road traffic vehicular networks

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E-Commerce Precision Marketing and Consumer Behavior Models Based on IoT clustering algorithm

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2022年第5期24卷 1-21页

作者： Guo, Shujuan Zhai, Rongbing Anhui Xinhua Univ Business Sch Hefei Peoples R China

This article aims to study e-commerce precision models and consumer behavior models based on clustering algorithms, and at the same time conduct detailed research on the Gaussian mixture distribution algorithm, consumer behavior and model construction, and precision marketing strategies in the clustering algorithm. First, a lot of analysis and demonstration of precision marketing strategies and the construction of consumer behavior models are carried out, and then the clustering algorithm -based electronic some experiments were carried out on the application of commercial precision marketing methods and consumer behavior models. The experimental results show that the precision marketing method using the clustering algorithm is more in line with the development of modern e-commerce. The application of the algorithm in the precision marketing methods of enterprises and consumer behavior models has promoted the vigorous development of enterprises, making the sales volume of enterprises reach 9.8%.

关键词： Behavior Model clustering algorithm E-Commerce Precision Marketing Methods

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INTUITIONISTIC FUZZY clustering algorithm BASED ON BOOLE MATRIX AND ASSOCIATION MEASURE

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2013年第1期12卷 95-118页

作者： Zhao, Hua Xu, Zeshui Wang, Zhong PLA Univ Sci & Technol Coll Sci Nanjing 210007 Jiangsu Peoples R China

In this paper we develop a measure for calculating the association coefficient between Atanassov's intuitionistic fuzzy sets (A-IFSs), and show its desirable axiomatic properties. Then we present an algorithm for clustering A-IFSs. The algorithm first utilizes the association coefficient of A-IFSs to construct an association matrix, and then calculates the lambda-cutting matrix of the association matrix no matter whether it is an equivalent matrix or not. After that, the lambda-cutting matrix is used to cluster A-IFSs (if the lambda-cutting matrix is just only a similarity matrix, then we can easily transform it into an equivalent matrix). Three examples are used to show the effectiveness of the association coefficient and the algorithm for clustering A-IFSs. Furthermore, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, we use another numerical example to illustrate the latter algorithm.

关键词： Atanassov's intuitionistic fuzzy set interval-valued intuitionistic fuzzy set association coefficients association matrix lambda-cutting matrix clustering algorithm

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