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Clustering with Weighted Hyperlink and Sub similarity matrix

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Journal of Beijing Institute of Technology 2006年第2期15卷 177-180页

作者：吴萍宋瀚涛张利萍吴正宇 School of Computer Science and Technology Beijing Institute of Technology

A web page clustering algorithm called PageCluster and the improved algorithm ImPageCluster solving overlapping are proposed. These methods not only take the web structure and page hyperlink into account, but also consider the importance of each page which is described as in-weight and out-weight. Compared with the traditional clustering methods, the experiments show that the runtimes of the proposed algorithms are less with the improved accuracies.

关键词： clustering web page hyperlink similarity matrix PageCluster ImPageCluster

Attribute Reduction for Information Systems via Strength of Rules and similarity matrix

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Computer Systems Science & Engineering 2023年第5期45卷 1531-1544页

作者： Mohsen Eid Tamer Medhat Manal E.Ali Department of Mathematics Faculty of ScienceTanta UniversityTantaEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Physics and Engineering Mathematics Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt

An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent *** are several ways for solving attribute reduction problems,but they all require a common *** selection of features in most scientific studies is a challenge for the *** working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases *** the other side,neglecting some attributes might jeopardize data *** this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some ***,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical *** the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each *** methods expand the options of attribute reductions for researchers.

关键词： Rough set reduction strength of rules similarity matrix

Distance-Based Regression Analysis for Measuring Associations

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Journal of Systems Science & Complexity 2023年第1期36卷 393-411页

作者： SHI Yuke ZHANG Wei LIU Aiyi LI Qizhai LSC Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Biostatistics and Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health Human Development National Institutes of HealthBethesdaMD 20847USA

Distance-based regression model,as a nonparametric multivariate method,has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies,genomic analyses,and many other research *** on it,a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the *** the best of our knowledge,the statistical properties of the pseudo-F statistic has not yet been well established in the *** fill this gap,the authors study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random *** that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large,the authors propose a square-root F-type test statistic which replaces the similarity matrix with its square *** asymptotic null distribution of the new test statistic and power of both tests are also *** studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-F *** test statistics are exemplified with a gene expression dataset for a prostate cancer pathway.

关键词： Asymptotic distribution chi-squared-type mixture nonparametric test pseudo-F test similarity matrix

Fault Feeder Identification in Non-effectively Grounded Distribution Network with Secondary Earth Fault

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1137-1148页

作者： Shu Zhang Tianlei Zang Wenhai Zhang Xianyong Xiao Department of Electrical Engineering Sichuan UniversityChengduChina

Secondary earth faults occur frequently in power distribution networks under harsh weather *** to its characteristics,a secondary earth fault is typically hidden within the transient of the first ***,most researchers tend to focus on a feeder with single fault while disregarding secondary *** paper presents a fault feeder identification method that considers secondary earth faults in a non-effectively grounded distribution ***,the wavelet singular entropy method is used to detect a secondary fault *** method can identify the moment at which a secondary fault *** zero-sequence current data can be categorized into two fault *** first and second fault stages correspond to the first and secondary faults,***,a similarity matrix containing the time-frequency transient information of the zero-sequence current at the two fault stages is defined to identify the fault ***,to confirm the effectiveness and reliability of the proposed method,we conduct simulation experiments and an adaptability analysis based on an electromagnetic transient program.

关键词： Secondary earth fault non-effectively grounded distribution network wavelet singular entropy similarity matrix zero-sequence current

A Novel Minkowski-distance-based Consensus Clustering Algorithm

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International Journal of Automation and computing 2017年第1期14卷 33-44页

作者： De-Gang Xu Pan-Lei Zhao Chun-Hua Yang Wei-Hua Gui Jian-Jun He College of Information Science and Engineering Central South University Changsha 410083 China

Consensus clustering is the problem of coordinating clustering information about the same data set coming from different runs of the same algorithm. Consensus clustering is becoming a state-of-the-art approach in an increasing number of applications. However, determining the optimal cluster number is still an open problem. In this paper, we propose a novel consensus clustering algorithm that is based on the Minkowski distance. Fusing with the Newman greedy algorithm in complex networks, the proposed clustering algorithm can automatically set the number of clusters. It is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. A numerical simulation is also given to demonstrate the effectiveness of the proposed algorithm. Finally, this consensus clustering algorithm is applied to a froth flotation process.

关键词： Minkowski distance consensus clustering similarity matrix process data froth flotation.

Trade-Off between Efficiency and Effectiveness: A Late Fusion Multi-View Clustering Algorithm

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Computers, Materials & Continua 2021年第3期66卷 2709-2722页

作者： Yunping Zhao Weixuan Liang Jianzhuang Lu Xiaowen Chen Nijiwa Kong National University of Defence Technology ChangshaChina Department of Mathematics Faculty of ScienceUniversity of SargodhaSargodhaPakistan

Late fusion multi-view clustering(LFMVC)algorithms aim to integrate the base partition of each single view into a consensus *** partitions can be obtained by performing kernel k-means clustering on all *** type of method is not only computationally efficient,but also more accurate than multiple kernel k-means,and is thus widely used in the multi-view clustering *** improves computational efficiency to the extent that the computational complexity of each iteration is reduced from Oen3T to OenT(where n is the number of samples).However,LFMVC also limits the search space of the optimal solution,meaning that the clustering results obtained are not ***,in order to obtain more information from each base partition and thus improve the clustering performance,we propose a new late fusion multi-view clustering algorithm with a computational complexity of *** on several commonly used datasets demonstrate that the proposed algorithm can reach quickly ***,compared with other late fusion algorithms with computational complexity of OenT,the actual time consumption of the proposed algorithm does not significantly *** the same time,comparisons with several other state-of-the-art algorithms reveal that the proposed algorithm also obtains the best clustering performance.

关键词： Late fusion kernel k-means similarity matrix

Clustering analysis of telecommunication customers

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The Journal of China Universities of Posts and Telecommunications 2009年第2期16卷 114-116,128页

作者： REN Hong ZHENG Yan WU Ye-rong School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China

In this article, a clustering method based on genetic algorithm （GA） for telecommunication customer subdivision is presented. First, the features of telecommunication customers （such as the calling behavior and consuming behavior） are extracted. Second, the similarities between the multidimensional feature vectors of telecommunication customers are computed and mapped as the distance between samples on a two-dimensional plane. Finally, the distances are adjusted to approximate the similarities gradually by GA. One advantage of this method is the independent distribution of the sample space. The experiments demonstrate the feasibility of the proposed method.

关键词： genetic algorithm similarity matrix customer subdivision

Efficient Preference Clustering via Random Fourier Features

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Big Data Mining and Analytics 2019年第3期2卷 195-204页

作者： Jingshu Liu Li Wang Jinglei Liu College of Data Science Taiyuan University of TechnologyJinzhong 030600China School of Computer and Control Engineering Yantai UniversityYantai 264005China

Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning *** approaches using the Nystr?m method,which randomly samples the training examples,we make use of random Fourier features,whose basis functions(i.e.,cosine and sine)are sampled from a distribution independent from the training sample set,to cluster preference data which appears extensively in recommender ***,we propose a two-stage preference clustering *** this framework,we make use of random Fourier features to map the preference matrix into the feature matrix,soon afterwards,utilize the traditional k-means approach to cluster preference data in the transformed feature *** with traditional preference clustering,our method solves the problem of insufficient memory and greatly improves the efficiency of the *** on movie data sets containing 100000 ratings,show that the proposed method is more effective in clustering accuracy than the Nystr?m and k-means,while also achieving better performance than these clustering approaches.

关键词： random Fourier features matrix decomposition similarity matrix Nystrom method preference clustering

Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

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Journal of Harbin Institute of Technology(New Series) 2015年第6期22卷 55-61页

作者： Ying Xia Zhongzhao Zhang Lin Ma Yao Wang School of Electronics and Information Engineering Harbin Institute of Technology Harbin 150080 China School of Communication and Electronic Engineering Qiqihar University Qiqihar 161006 Heilongjiang China

With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online *** this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.

关键词： wireless local area network(WLAN) semi-supervised similarity matrix clustering affinity propagation