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检索条件"主题词=clustering Algorithm"

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A New Hierarchical clustering algorithm with Intersection Points 5

A New Hierarchical Clustering Algorithm with Intersection Po...

引用

5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)

作者： Nazari, Zahra Kang, Dongshik Univ Ryukyus Grad Sch Engn & Sci Okinawa Japan Univ Ryukyus Dept Informat Engn Okinawa Japan

ISBN: (纸本)9781538650028

This paper builds upon our previous paper that has introduced a new hierarchical clustering algorithm. In this paper we attempted to solve algorithmic defects and use more validation measures to show the strength of our proposed algorithm. The main purpose of this clustering algorithm is to provide a better clustering quality and higher accuracy utilizing intersection points. To validate our clustering algorithm, we have performed several experiments with benchmark datasets. Besides our proposed algorithm, five well-known agglomerative clustering algorithms are also used. Purity as an external criterion is used to evaluate the performance of clustering algorithms. Compactness of each cluster derived by clustering algorithms is also calculated to evaluate the validity of clustering algorithms. Eventually, the results of experiments show that in most cases the error rate of our proposed algorithm is lower than other clustering algorithms which are used in this study.

关键词： Data mining unsupervised learning cluster analysis clustering algorithm hierarchical clustering

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A Weighted clustering algorithm Based on Node Energy for Multi-UAV Ad Hoc Networks 10

A Weighted Clustering Algorithm Based on Node Energy for Mul...

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10th International Conference on Information Optics and Photonics

作者： Liu, Jinchao Zhang, Qi Xin, Xiangjun Tian, Qinghua Tao, Ying Ding, Rui Shen, Yufei Cao, Guixing Liu, Naijin Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Space Ground Interconnect & Conve Beijing 100876 Peoples R China China Acad Space Technol Beijing 100094 Peoples R China

ISBN: (纸本)9781510625808

In order to solve the problem of endurance of high-speed mobile multi-UAV in Ad Hoc networks with frequent network topology changing, this paper proposes a weighted clustering algorithm based on node energy (EWCA). In this algorithm, we use a multi-parameter weighted clustering algorithm, which improve the node degree difference and node residual energy calculation methods, and study the similarity between the adjacent nodes in terms of speed, direction, etc. The simulation studies the inter-cluster switching rate, the number of nodes and the performance of the minimum lifetime of network node. The results show that, compared with the highest node degree algorithm(HIGHD), adaptive security clustering algorithm(SWCA) and weighted clustering algorithm(WCA), the proposed algorithm can reduce the number of clusters, improve the stability of clustering, and the survival time of drones, and improve the network's endurance.

关键词： Ad Hoc networks clustering algorithm node energy

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Mechanical Fault Diagnosis of On-load Tap Changer of Power Transformer by clustering algorithm 10

Mechanical Fault Diagnosis of On-load Tap Changer of Power T...

引用

IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Chen, Peilong Ma, Xiaohong Wang, Fenghua Xie, Fangxin Zhou, Dongxu Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Guizhou Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781538656860

On-load tap changer(OLTC) is an important component in power transformer for voltage regulation, and mechanical fault is the main problem during its switch-over process. Vibration signals resulted from the operation of OLTC are closely related to its mechanical condition. To better recognize the incipient mechanical faults of OLTC with high accuracy, two kinds of typical mechanical faults of storage spring and static contact are simulated in an CM type OLTC with the measured vibration signals, which are always chaotic. Then the one-dimension vibration signal time series is reconstructed into high dimension phase space with the calculated delay time and embedding dimension. The improved K-means clustering algorithm is applied to classify the large amount of phase points into different clustering. The curve of weighted distribution information entropy of each clustering is calculated. Calculated results have shown that the defined information entropy curve is capable of identify the different mechanical fault of OLTC.

关键词： clustering algorithm fault diagnosis On-load Tap Changer power transformer vibration signals

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The Parameter Configuration Method of DBSCAN clustering algorithm 5

The Parameter Configuration Method of DBSCAN Clustering Algo...

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5th International Conference on Systems and Informatics (ICSAI)

作者： Song, Jin-yu Guo, Yi-ping Wang, Bin Army Engn Univ PLA Coll Command Syst Engn Nanjing Jiangsu Peoples R China Army Engn Univ PLA Teaching Examinat Ctr Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728101200

This paper discusses the key problem of the application of DBSCAN clustering algorithm in the detection of abnormal data, that is the configuration problem for two threshold values of Eps and Minpts. A method has been proposed, which is based on a visual display of the statistical characteristic of the dataset itself. Using the MATLAB tool, we implement the DBSCAN algorithm and show statistical characteristic of the dataset. The standard dataset is used to verify the proposed method. The experiment results show that the proposed method can make DBSCAN algorithm get better clustering result.

关键词： exception detection clustering algorithm DBSCAN parameter configuration

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K-means parallel multi-relational clustering algorithm for spatial data

K-means parallel multi-relational clustering algorithm for s...

引用

2019 2nd International Conference on Computer Science and Advanced Materials(CSAM 2019)

作者： Huang Suyu Wang Song School of Computer Science Wuhan Donghu University

This paper is a study of parallel clustering algorithm K-means. Firstly, the design idea of K-means clustering algorithm on a single computer is introduced. Secondly, the design idea of K-means clustering algorithm in cluster environment is elaborated in detail. When K-means clustering algorithm is faced with massive data, the complexity of time and space has become the bottleneck of K-means clustering algorithm. On the basis of fully studying the traditional K-Means clustering algorithm, this paper presents the design idea of parallel K-Means clustering algorithm, and gives the estimation formula of its acceleration ratio. The correctness and validity of the algorithm are proved by experiments.

关键词： clustering algorithm parallel relational spatial data cluster

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CONTRAST OF GAUSSIAN MIXTURE MODEL AND clustering algorithm FOR SINGER IDENTIFICATION 8

CONTRAST OF GAUSSIAN MIXTURE MODEL AND CLUSTERING ALGORITHM ...

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8th International Conference on Computer Communication and Informatics (ICCCI)

作者： Dharini, D. Revathy, A. Kalaivani, M. K Ramakrishnan Coll Engn Elect & Commun Engn Tiruchirappalli Tamil Nadu India Sastra Univ Elect & Commun Engn Thanjavur Tamil Nadu India

ISBN: (纸本)9781538622384

The intension is to provide the contrast between clustering algorithm and Gaussian Mixture Model using Perceptual Linear Prediction features to assess the singer identification structure using two phases, phase 1 as training and phase 2 as testing over the film tracks(vocal with background music). The intent of assessing of singer is to categorize different singers impartial of data that is trained in phase 1. The aspects for two phases are executed for downright tracks from films for 20 different singers. In phase 1 aspects, for individual singer 15 tracks are loaded as input data. Now loaded datas are shaped to go through a deck of pre-handling steps. The pre-handling steps includes three more internal stages with stage1 as Pre-emphasis, stage2 as Frame Blocking and stage3 as Windowing. From individual context of pre-handled signal PLP features are evolved. Using the K-Means clustering algorithm and GMM the phase1 output is developed for individual singers. In clustering algorithm the singer is categorized deployed with choice of the model that gives mean value as minimum. In GMM, by using Maximum Likelihood (ML) algorithm singers are categorized deployed with choice of the model that gives maximum likelihood. Depending on identity of accuracy the singer identification structure is performed.

关键词： clustering algorithm GMM(Gaussian Mixture Model) SID(Singer Identification)

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A Mobility-Based Double-Head clustering algorithm for Dynamic VANET

A Mobility-Based Double-Head Clustering Algorithm for Dynami...

引用

6th International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC)

作者： Alsuhli, Ghada H. Khattab, Ahmed Fahmy, Yasmine A. Cairo Univ Elect & Elect Commun Engn Dept Giza Egypt

ISBN: (纸本)9781538692301

Vehicular Ad Hoc Network (VANET) is a promising technology that still faces many challenges such as scalability and the highly dynamic topology. An effective VANET clustering algorithm significantly relieves the effect of these challenges. In this paper, we propose a double-head clustering (DHC) algorithm for VANETs. Our proposed approach is a mobility-based clustering algorithm that exploits the most relevant mobility metrics such as vehicles' speed, position and direction, in addition to other metrics related to the communication link quality in order to achieve stable clusters. We compare the proposed algorithm against existing clustering algorithms using different evaluation metrics under dynamic and static mobility scenarios. The proposed algorithm proves its stability and efficiency under different mobility scenarios.

关键词： VANET ITS clustering algorithm cluster head selection mobility

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A Deep clustering algorithm Based on Self-organizing Map Neural Network 14th

A Deep Clustering Algorithm Based on Self-organizing Map Neu...

引用

14th International Conference on Intelligent Computing (ICIC)

作者： Tao, Yanling Li, Ying Lin, Xianghong Northwest Normal Univ Coll Social Dev & Publ Adm Lanzhou 730070 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9783319959573;9783319959566

clustering is one of the most basic unsupervised learning problems in the field of machine learning and its main goal is to separate data into clusters with similar data points. Because of various redundant and complex structures for the raw data, the general algorithm usually is difficult to separate different clusters from the data and the effect is not obvious. Deep learning is a technology that automatically learns nonlinear and more conducive clustering features from complex data structures. This paper presents a deep clustering algorithm based on self-organizing map neural network. This method combines the feature learning ability of stacked auto-encoder from the raw data and feature clustering with unsupervised learning of self-organizing map neural network. It is aim to achieve the greatest separability for the data space. Through the experimental analysis and comparison, the proposed algorithm has better recognition rate, and improves the clustering performance on low and high dimension data.

关键词： clustering algorithm Deep neural networks Stacked auto-encoders Self-organizing map neural network

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Corporate Networks Traffic Analysis for Knowledge Management Based on Random Interactions clustering algorithm 13th

Corporate Networks Traffic Analysis for Knowledge Management...

引用

13th International Conference on Knowledge Management in Organizations (KMO)

作者： Rodriguez Rodriguez, Jorge Enrique Medina Garcia, Victor Hugo Ospina Usaquen, Miguel Angel Dist Univ Francisco Jose de Caldas Bogota Colombia Manuela Beltran Univ Bogota Colombia

ISBN: (纸本)9783319952048;9783319952031

Categorization of network traffic according to application is an element that determines important task in management network such as flow prioritization according;consist on the importance of the application to determine which flows get the most resources or which flows get resources first, traffic policing consists in drops traffic and diagnostic monitoring. In the other hand, categorize applications can be employed for resolve engineering problems such as workload categorization for identify the amount of work assigned or done by a client, capacity planning and route provisioning. In this paper, we show the results of the research project "Implementation of the RAIN algorithm for the clustering of network users". It describes the problem, and mentions some related researches. Then, the selected gravitation algorithm is itemized;it also submits the tests analysis and results, and finally shows the reached conclusions from the research.

关键词： Gravitation clustering clustering algorithm Machine learning Data mining Network traffic

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Density based spatio-temporal trajectory clustering algorithm 38

Density based spatio-temporal trajectory clustering algorith...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Cheng, Zhiyuan Jiang, Ling Liu, Desheng Zheng, Zezhong Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Ohio State Univ Dept Geog Columbus OH 43210 USA

ISBN: (纸本)9781538671504

In recent years, with the development of mobile location services and cloud computing technology, the collection and processing of mobile location information has become a reality. The mass database which is composed of mobile location data has promoted the development of the research on mobile location data. It is very important for us to understand the spatial distribution and temporal characteristics of moving patterns and identify the mechanism of motion formation, predict the future development of sports through trajectory clustering analysis. At present, trajectory clustering research mainly focuses on the spatial position changes of moving objects. Temporal constraints in spatial and temporal clustering are generally auxiliary information, but do not really participate in clustering. In this paper, a clustering algorithm for trajectory data based on spatiotemporal pattern is proposed. First, the curve edge detection method is used to extract the trajectory feature points. Then the trajectory is divided into sub track segments according to the trajectory feature points. Finally, the density based clustering algorithm is applied to cluster according to the temporal and spatial similarity between sub trajectories. The Hot spot analysis experimental based on Chengdu taxi GPS track data results show that the similarity measurement based on spatiotemporal features can get better clustering results because of both the spatial and temporal characteristics of the trajectory.

关键词： Spatiotemporal trajectory similarity measure clustering algorithm density clustering

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