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Journal articles on the topic 'Clustering en ligne'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Bouguettaya, A. "On-line clustering." IEEE Transactions on Knowledge and Data Engineering 8, no. 2 (1996): 333–39. http://dx.doi.org/10.1109/69.494170.

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2

FANG, LI ZHI. "QUASAR CLUSTERING AND ITS COSMOLOGICAL IMPLICATION." International Journal of Modern Physics A 04, no. 14 (1989): 3477–502. http://dx.doi.org/10.1142/s0217751x89001394.

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The clusterings of quasars and absorption line clouds have been analyzed from the viewpoint of the structure formation of the universe. It was found that the features of quasar clustering are quite different from those of galaxies. These results have already given several meaningful constraints on the structure formation, as follows: (a) quasar clustering is much weaker than in galaxies; (b) large scale structures, such as superclusters, should probably be formed after the epoch z~2; (c) the amplitude of the total density inhomogeneity seems to be less than that of galaxy distribution by at le

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3

El-Sonbaty, Yasser, and M. A. Ismail. "On-line hierarchical clustering." Pattern Recognition Letters 19, no. 14 (1998): 1285–91. http://dx.doi.org/10.1016/s0167-8655(98)00104-4.

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4

Mańka-Krasoń, Anna, Advera Mwijage, and Krzysztof Kułakowski. "Clustering in random line graphs." Computer Physics Communications 181, no. 1 (2010): 118–21. http://dx.doi.org/10.1016/j.cpc.2009.09.010.

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5

McLean, G. F., and D. Kotturi. "Vanishing point detection by line clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 11 (1995): 1090–95. http://dx.doi.org/10.1109/34.473236.

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6

Inui, Kentaro, Shun'ichi Kaneko, and Satoru Igarashi. "Robust line fitting using LMedS clustering." Systems and Computers in Japan 34, no. 14 (2003): 92–100. http://dx.doi.org/10.1002/scj.1225.

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7

Younis, O., and S. Fahmy. "FlowMate: scalable on-line flow clustering." IEEE/ACM Transactions on Networking 13, no. 2 (2005): 288–301. http://dx.doi.org/10.1109/tnet.2005.845532.

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8

Butler, Ronald W. "Optimal clustering on the real line." Journal of Multivariate Analysis 24, no. 1 (1988): 88–108. http://dx.doi.org/10.1016/0047-259x(88)90104-2.

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9

Yu, Won Pil, Gil Whoan Chu, and Myung Jin Chung. "A robust line extraction method by unsupervised line clustering." Pattern Recognition 32, no. 4 (1999): 529–46. http://dx.doi.org/10.1016/s0031-3203(98)00100-9.

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10

Kim, Seong-Jun, Byung Hak Choe, and Woosik Kim. "A Prediction Method of the Gas Pipeline Failure Using In-line Inspection and Corrosion Defect Clustering." Journal of Korean Institute of Intelligent Systems 24, no. 6 (2014): 651–56. http://dx.doi.org/10.5391/jkiis.2014.24.6.651.

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11

Vijendra, Singh, and Sahoo Laxman. "Symmetry Based Automatic Evolution of Clusters: A New Approach to Data Clustering." Computational Intelligence and Neuroscience 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/796276.

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We present a multiobjective genetic clustering approach, in which data points are assigned to clusters based on new line symmetry distance. The proposed algorithm is called multiobjective line symmetry based genetic clustering (MOLGC). Two objective functions, first the Davies-Bouldin (DB) index and second the line symmetry distance based objective functions, are used. The proposed algorithm evolves near-optimal clustering solutions using multiple clustering criteria, without a priori knowledge of the actual number of clusters. The multiple randomizedKdimensional (Kd) trees based nearest neigh

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12

Hwang, Jae-Ho. "Feature Extraction by Line-clustering Segmentation Method." KIPS Transactions:PartB 13B, no. 4 (2006): 401–8. http://dx.doi.org/10.3745/kipstb.2006.13b.4.401.

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13

Ravankar, Ankit A., Abhijeet Ravankar, Takanori Emaru, and Yukinori Kobayashi. "Clustering Based Line Detection in Noisy Datasets." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 1P1–07a4. http://dx.doi.org/10.1299/jsmermd.2016.1p1-07a4.

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14

Fernandes, Cristina G., and Marcio T. I. Oshiro. "Kinetic clustering of points on the line." Theoretical Computer Science 639 (August 2016): 60–71. http://dx.doi.org/10.1016/j.tcs.2016.05.020.

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15

Žalik, Krista Rizman, and Borut Žalik. "A sweep-line algorithm for spatial clustering." Advances in Engineering Software 40, no. 6 (2009): 445–51. http://dx.doi.org/10.1016/j.advengsoft.2008.06.003.

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16

Nurzahputra, Aldi, Afrizal Rizqi Pranata, and Aji Puwinarko. "Decision Support System for Football Players Lineup Selection using Fuzzy Multiple Attribute Decision Making and K-Means Clustering Methods." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (2017): 106–9. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.106-109.

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In football, the selection of players line-up is based on their statistical performance. In this research, the line-up selection can implement the decision support system (DSS) with FMADM SAW method. The criteria were used are goal, assists, saves, clean sheets, yellow cards, red cards, games, and an own goal. Then, the assessment players performance is using K-Means Clustering. There are two clusters: cluster_cukup and cluster_baik. The system used Manchester City player data in Forward, Midfielder, Defender and Goal Keeper position. The purpose of this research is applying the FMADM and K-Me

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17

Kachi, D. "From invariant line features clustering to line matching: theory and applications." Journal of Electronic Imaging 8, no. 2 (1999): 185. http://dx.doi.org/10.1117/1.482696.

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18

Krumpe, Mirko, Takamitsu Miyaji, and Alison L. Coil. "Clustering Measurements of broad-line AGNs: Review and Future." Acta Polytechnica CTU Proceedings 1, no. 1 (2014): 71–78. http://dx.doi.org/10.14311/app.2014.01.0071.

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Despite substantial effort, the precise physical processes that lead to the growth of super-massive black holes in the centers of galaxies are still not well understood. These phases of black hole growth are thought to be of key importance in understanding galaxy evolution. Forthcoming missions such as eROSITA, HETDEX, eBOSS, BigBOSS, LSST, and Pan-STARRS will compile by far the largest ever Active Galactic Nuclei (AGNs) catalogs which will allow us to measure the spatial distribution of AGNs in the universe with unprecedented accuracy. For the first time, AGN clustering measurements will reac

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19

Wan, Fang, and Fei Deng. "Using Line Segment Clustering to Detect Vanishing Point." Advanced Materials Research 268-270 (July 2011): 1553–58. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1553.

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Vanishing point detection is a basic work in camera self-calibration, single view reconstruction and series of images matching. Our research is based on line segments clustering method. First, we scan the image with edge detection algorithm for series of line segments. Then, we construct a similar concept space to classify the segments according to the vector distances. At last, we can use each cluster of the line segments to estimate the responsible vanishing point. For the clusters of the line segments indicate the main direction in multiple lines, the detected vanishing points are principal

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20

Piciarelli, C., and G. L. Foresti. "On-line trajectory clustering for anomalous events detection." Pattern Recognition Letters 27, no. 15 (2006): 1835–42. http://dx.doi.org/10.1016/j.patrec.2006.02.004.

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21

Fernández, Carlos, Vidal Moreno, Belen Curto, and J. Andres Vicente. "Clustering and line detection in laser range measurements." Robotics and Autonomous Systems 58, no. 5 (2010): 720–26. http://dx.doi.org/10.1016/j.robot.2009.10.008.

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22

Pensa, Ruggero G., Dino Ienco, and Rosa Meo. "Hierarchical co-clustering: off-line and incremental approaches." Data Mining and Knowledge Discovery 28, no. 1 (2012): 31–64. http://dx.doi.org/10.1007/s10618-012-0292-8.

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23

Saha, Sriparna, and Sanghamitra Bandyopadhyay. "On principle axis based line symmetry clustering techniques." Memetic Computing 3, no. 2 (2010): 129–44. http://dx.doi.org/10.1007/s12293-010-0049-0.

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24

Tuerxun, Palidan, Kamil Moydin, Abdusalam Dawut, Aysadet Abliz, and Askar Hamdulla. "Clustering-based word segmentation from off-line handwritten Uyghur text-line images." International Journal of Information and Communication Technology 16, no. 3 (2020): 214. http://dx.doi.org/10.1504/ijict.2020.10027481.

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Hamdulla, Askar, Aysadet Abliz, Abdusalam Dawut, Kamil Moydin, and Palidan Tuerxun. "Clustering-based word segmentation from off-line handwritten Uyghur text-line images." International Journal of Information and Communication Technology 16, no. 3 (2020): 214. http://dx.doi.org/10.1504/ijict.2020.106312.

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26

Guedalia, Isaac David, Mickey London, and Michael Werman. "An On-Line Agglomerative Clustering Method for Nonstationary Data." Neural Computation 11, no. 2 (1999): 521–40. http://dx.doi.org/10.1162/089976699300016755.

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An on-line agglomerative clustering algorithm for nonstationary data is described. Three issues are addressed. The first regards the temporal aspects of the data. The clustering of stationary data by the proposed algorithm is comparable to the other popular algorithms tested (batch and on-line). The second issue addressed is the number of clusters required to represent the data. The algorithm provides an efficient framework to determine the natural number of clusters given the scale of the problem. Finally, the proposed algorithm implicitly minimizes the local distortion, a measure that takes

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27

Kumar, Vijay, Jitender Kumar Chhabra, and Dinesh Kumar. "Performance Evaluation of Line Symmetry-Based Validity Indices on Clustering Algorithms." Journal of Intelligent Systems 26, no. 3 (2017): 483–503. http://dx.doi.org/10.1515/jisys-2016-0010.

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AbstractFinding the optimal number of clusters and the appropriate partitioning of the given dataset are the two major challenges while dealing with clustering. For both of these, cluster validity indices are used. In this paper, seven widely used cluster validity indices, namely DB index, PS index, I index, XB index, FS index, K index, and SV index, have been developed based on line symmetry distance measures. These indices provide the measure of line symmetry present in the partitioning of the dataset. These are able to detect clusters of any shape or size in a given dataset, as long as they

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28

Tao, Lv, Yongtao Hao, Hao Yijie, and Shen Chunfeng. "K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3096917.

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Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the

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29

Sewell, P. D., T. M. Benson, C. Christopoulos, D. W. P. Thomas, A. Vukovic, and J. G. Wykes. "Implicit Element Clustering for Tetrahedral Transmission-Line Modeling (TLM)." IEEE Transactions on Microwave Theory and Techniques 57, no. 8 (2009): 2005–14. http://dx.doi.org/10.1109/tmtt.2009.2025451.

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30

Schunck, B. G. "Image flow segmentation and estimation by constraint line clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 10 (1989): 1010–27. http://dx.doi.org/10.1109/34.42834.

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31

SUEOKA, Yuichiro, Takuto KITA, Masato ISHIKAWA, and Koichi OSUKA. "Harnessing control of sheepdog agents by on-line clustering." IEICE Proceeding Series 2 (March 17, 2014): 49–52. http://dx.doi.org/10.15248/proc.2.49.

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32

Khy, Sophoin, Yoshiharu Ishikawa, and Hiroyuki Kitagawa. "A Novelty-based Clustering Method for On-line Documents." World Wide Web 11, no. 1 (2007): 1–37. http://dx.doi.org/10.1007/s11280-007-0018-9.

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33

Subramani, Kalaivani, Shantharajah Periyasamy, and Padma Theagarajan. "Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 8 (2019): 769–76. http://dx.doi.org/10.2174/1573405614666180322130242.

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Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experienc

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34

You, Kwan-Hee, Young-Mi Ji, and O.-Yu Kwon. "Clusterin Overexpression is Responsible for the Anti-apoptosis Effect in a Mouse Neuroblastoma Cell Line, B103." Zeitschrift für Naturforschung C 58, no. 1-2 (2003): 148–52. http://dx.doi.org/10.1515/znc-2003-1-226.

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The functional role of clusterin in apoptosis was examined using flow cytometry. Clusterin cDNA was transfected into the mouse neuroblastoma cell line, B103, in order to determine if clusterin overexpression inhibits apoptosis. The increased clusterin expression level in the B103 cells tended to suppress the apoptotic index. This suggests an association of clusterin gene expression with apoptosis inhibition. These results support the conclusion that clusterin expression in B103 cells has an antiapoptotic influence

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35

Yang, Ya Feng, Ai Min Yang, and Huan Cheng Zhang. "Application of Set Pair Analysis in the Material Clustering." Applied Mechanics and Materials 443 (October 2013): 707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.443.707.

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Based on the Set Pair Analysis theory and fuzzy set, the traditional clustering methods were taken into account from the characteristics of the forward, reverse and uncertain factors. A new cluster analysis method was proposed which allows more flexible clustering analysis. A case study in the materials clustering was carried on for the consumers to look for the most benefits in line with their own requirements.

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36

Bo, Guan, Liang Xu Liu, Jian Bo Fan, and Jin Yang Chen. "An Efficient Trajectory Clustering Framework Based Relative Distance." Applied Mechanics and Materials 241-244 (December 2012): 3209–12. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3209.

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along with more and more trajectory dataset being collected into application servers, the research in trajectory clustering has become increasingly important topic. This paper proposes a new mobile object trajectory Clustering algorithm (Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation, TraClustMHD). In this framework, improved Minimum Hausdorff Distance under Translation is presented to measure the similarity between sub-segments. In additional, R-Tree is employed to improve the efficiency. The experimental results showed that this algorithm better than based

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37

Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, mi

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38

Coffman, E. G., P. J. Courtois, E. N. Gilbert, and Ph Piret. "A distributed clustering process." Journal of Applied Probability 28, no. 4 (1991): 737–50. http://dx.doi.org/10.2307/3214677.

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The points of a graph G will form clusters as a result of a flow process. Initially, points i of G own resources xi which are i.i.d. random real numbers. Afterwards, resources flow between points, but always from a point to a neighbor that has accumulated a larger total resource. Thus points with small resource tend to lose it and points with large resource tend to gain. Eventually the flow stops with only two kinds of points, nulls with no resource left and absorbers with such large resource that no neighbor can take it. The final resource at an absorber is a sum of certain initial resources

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Coffman, E. G., P. J. Courtois, E. N. Gilbert, and Ph Piret. "A distributed clustering process." Journal of Applied Probability 28, no. 04 (1991): 737–50. http://dx.doi.org/10.1017/s0021900200042650.

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The points of a graph G will form clusters as a result of a flow process. Initially, points i of G own resources xi which are i.i.d. random real numbers. Afterwards, resources flow between points, but always from a point to a neighbor that has accumulated a larger total resource. Thus points with small resource tend to lose it and points with large resource tend to gain. Eventually the flow stops with only two kinds of points, nulls with no resource left and absorbers with such large resource that no neighbor can take it. The final resource at an absorber is a sum of certain initial resources

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40

Banu, P. K. Nizar, and S. Andrews. "Performance Analysis of Hard and Soft Clustering Approaches For Gene Expression Data." International Journal of Rough Sets and Data Analysis 2, no. 1 (2015): 58–69. http://dx.doi.org/10.4018/ijrsda.2015010104.

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Mining gene expression data is growing rapidly to predict gene expression patterns and assist clinicians in early diagnosis of tumor formation. Clustering gene expression data is the most important phase, helps in finding group of genes that are highly expressed and suppressed. This paper analyses the performance of most representative hard and soft off-line clustering algorithms: K-Means, Fuzzy C-Means, Self Organizing Maps (SOM) based clustering and Genetic Algorithm (GA) based clustering for brain tumor gene expression dataset. Clusters produced by the clustering algorithms are the indicati

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41

Pasupuleti, Rajesh, and Narsimha Gugulothu. "PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 5 (2013): 3443–51. http://dx.doi.org/10.24297/ijct.v12i5.2914.

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Clustering analysis initiativesÂ a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of theÂ requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected byÂ user.Â In this paper, we proposed new clustering method based on linear approximation of

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42

Huang, Yuchun, Yingli Du, and Wenxuan Shi. "Fast and Accurate Power Line Corridor Survey Using Spatial Line Clustering of Point Cloud." Remote Sensing 13, no. 8 (2021): 1571. http://dx.doi.org/10.3390/rs13081571.

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High-voltage and ultra-high-voltage overhead power lines are important to meet the electricity demand of our daily activities and productions. Due to the overgrowth of trees/vegetation within the corridor area, the distance between the power lines and its surroundings may break through the safety threshold, which could cause potential hazards such as discharge and fire. To ensure the safe and stable operation of the power lines, it is necessary to survey them regularly so that the potential hazards from the surroundings within the power line corridor could be investigated timely. This paper is

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43

Xu, Yan, Jiangtao Dong, Zishuo Han, and Peiguang Wang. "Multichannel Correlation Clustering Target Detection." Information Technology And Control 49, no. 3 (2020): 335–45. http://dx.doi.org/10.5755/j01.itc.49.3.25507.

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During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is esta

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44

Ya-Jun Zhang and Zhi-Qiang Liu. "Self-splitting competitive learning: a new on-line clustering paradigm." IEEE Transactions on Neural Networks 13, no. 2 (2002): 369–80. http://dx.doi.org/10.1109/72.991422.

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CHUNG, RONALD, and KIN-LAP LEUNG. "AN ITERATIVE CLUSTERING PROCEDURE FOR INTERPRETING AN IMPERFECT LINE DRAWING." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 08 (1996): 867–86. http://dx.doi.org/10.1142/s0218001496000505.

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Recovering three-dimensional shape of an object from a single line drawing is a classical problem in computer vision. Methods proposed range from Huffman–Clowes junction labeling, to Kanade's gradient space and skew symmetry analysis, to Sugihara's necessary and sufficient condition for a realizable polyhedral object, to Marill's MSDA shape recovery procedure, to Leclerc–Fischler's shape recovery procedure which assures planar faces, and to the recent Baird–Wang's gradient-descent algorithm which has a favorable time complexity. Yet all these assume perfect line drawings as the input. We propo

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Martínez, Boris, Jesús Fernández, Erick Marichal, and Francisco Herrera. "FUZZY MODELLING OF NONLINEAR SYSTEMS USING ON LINE CLUSTERING METHODS." IFAC Proceedings Volumes 40, no. 1 (2007): 256–61. http://dx.doi.org/10.3182/20070213-3-cu-2913.00044.

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Maxime, Redondin, Bouillaut Laurent, and Daucher Dimitri. "A Clustering-based Approach to Segment a Pavement Markings Line." International Journal of Performability Engineering 16, no. 10 (2020): 1497. http://dx.doi.org/10.23940/ijpe.20.10.p1.14971508.

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48

Macedo, Mariana G. M., Elliackin M. N. Figueiredo, Fabiana M. B. Soares, et al. "Clustering students based on grammatical errors for on-line education." Learning and Nonlinear Models 16, no. 1 (2018): 26–40. http://dx.doi.org/10.21528/lnlm-vol16-no1-art2.

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Zhong–zhou, Ren, and Xu Gong–ou. "Triton–Clustering in Light Nuclei Near the Neutron Drip Line." Communications in Theoretical Physics 14, no. 2 (1990): 231–34. http://dx.doi.org/10.1088/0253-6102/14/2/231.

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50

Bianchi, Davide, Héctor Gil-Marín, Rossana Ruggeri, and Will J. Percival. "Measuring line-of-sight-dependent Fourier-space clustering using FFTs." Monthly Notices of the Royal Astronomical Society: Letters 453, no. 1 (2015): L11—L15. http://dx.doi.org/10.1093/mnrasl/slv090.

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