Academic literature on the topic 'Method of k-means'
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Journal articles on the topic "Method of k-means"
Hedar, Abdel-Rahman, Abdel-Monem Ibrahim, Alaa Abdel-Hakim, and Adel Sewisy. "K-Means Cloning: Adaptive Spherical K-Means Clustering." Algorithms 11, no. 10 (October 6, 2018): 151. http://dx.doi.org/10.3390/a11100151.
Full textMaldonado, Sebastián, Emilio Carrizosa, and Richard Weber. "Kernel Penalized K-means: A feature selection method based on Kernel K-means." Information Sciences 322 (November 2015): 150–60. http://dx.doi.org/10.1016/j.ins.2015.06.008.
Full textLitvinenko, Natalya, Orken Mamyrbayev, Assem Shayakhmetova, and Mussa Turdalyuly. "Clusterization by the K-means method when K is unknown." ITM Web of Conferences 24 (2019): 01013. http://dx.doi.org/10.1051/itmconf/20192401013.
Full textHämäläinen, Joonas, Tommi Kärkkäinen, and Tuomo Rossi. "Improving Scalable K-Means++." Algorithms 14, no. 1 (December 27, 2020): 6. http://dx.doi.org/10.3390/a14010006.
Full textKim, Ga-On, Gang-Seong Lee, and Sang-Hun Lee. "An Edge Extraction Method Using K-means Clustering In Image." Journal of Digital Convergence 12, no. 11 (November 28, 2014): 281–88. http://dx.doi.org/10.14400/jdc.2014.12.11.281.
Full textArthur, David, Bodo Manthey, and Heiko Röglin. "Smoothed Analysis of the k-Means Method." Journal of the ACM 58, no. 5 (October 2011): 1–31. http://dx.doi.org/10.1145/2027216.2027217.
Full textSARMA, T. HITENDRA, P. VISWANATH, and B. ESWARA REDDY. "Single pass kernel k-means clustering method." Sadhana 38, no. 3 (June 2013): 407–19. http://dx.doi.org/10.1007/s12046-013-0143-3.
Full textHar-Peled, Sariel, and Bardia Sadri. "How Fast Is the k-Means Method?" Algorithmica 41, no. 3 (December 8, 2004): 185–202. http://dx.doi.org/10.1007/s00453-004-1127-9.
Full textD. Indriyanti, A., D. R. Prehanto, and T. Z. Vitadiar. "K-means method for clustering learning classes." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 835. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp835-841.
Full textCho, Young-Sung, Mi-Sug Gu, and Keun-Ho Ryu. "Development of Personalized Recommendation System using RFM method and k-means Clustering." Journal of the Korea Society of Computer and Information 17, no. 6 (June 30, 2012): 163–72. http://dx.doi.org/10.9708/jksci.2012.17.6.163.
Full textDissertations / Theses on the topic "Method of k-means"
Кіріченко, Л. О., В. Г. Кобзєв, and Є. Д. Федоренко. "Data Mining methods for detection of collective anomalies in time series." Thesis, Національна академія Національної гвардії України, 2021. https://openarchive.nure.ua/handle/document/16449.
Full textHudson, Cody Landon. "Protein structure analysis and prediction utilizing the Fuzzy Greedy K-means Decision Forest model and Hierarchically-Clustered Hidden Markov Models method." Thesis, University of Central Arkansas, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1549796.
Full textStructural genomics is a field of study that strives to derive and analyze the structural characteristics of proteins through means of experimentation and prediction using software and other automatic processes. Alongside implications for more effective drug design, the main motivation for structural genomics concerns the elucidation of each protein’s function, given that the structure of a protein almost completely governs its function. Historically, the approach to derive the structure of a protein has been through exceedingly expensive, complex, and time consuming methods such as x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy.
In response to the inadequacies of these methods, three families of approaches developed in a relatively new branch of computer science known as bioinformatics. The aforementioned families include threading, homology-modeling, and the de novo approach. However, even these methods fail either due to impracticalities, the inability to produce novel folds, rampant complexity, inherent limitations, etc. In their stead, this work proposes the Fuzzy Greedy K-means Decision Forest model, which utilizes sequence motifs that transcend protein family boundaries to predict local tertiary structure, such that the method is cheap, effective, and can produce semi-novel folds due to its local (rather than global) prediction mechanism. This work further extends the FGK-DF model with a new algorithm, the Hierarchically Clustered-Hidden Markov Models (HC-HMM) method to extract protein primary sequence motifs in a more accurate and adequate manner than currently exhibited by the FGK-DF model, allowing for more accurate and powerful local tertiary structure predictions. Both algorithms are critically examined, their methodology thoroughly explained and tested against a consistent data set, the results thereof discussed at length.
Ruzgys, Martynas. "IT žinių portalo statistikos modulis pagrįstas grupavimu." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_143545-16583.
Full textPresented data mining methods and clustering usage in current statistical systems and created statistics module prototype for data storage, analysis and visualization for IT knowledge portal. In suggested statistics prototype database periodical data transformations are performed. Statistical data accessed in portal can be clustered. Clustered information represented graphically may serve for interpreting information when trends may be noticed. One of the best known data clustering methods – parallel k-means method – is adapted for separating similar data clusters.
紘幸, 児玉, and Hiroyuki Kodama. "工具カタログからのデータマイニングに支援されたものづくりシステムに関する研究." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12863871/?lang=0, 2014. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12863871/?lang=0.
Full textŽambochová, Marta. "Shluková analýza rozsáhlých souborů dat: nové postupy založené na metodě k-průměrů." Doctoral thesis, Vysoká škola ekonomická v Praze, 2005. http://www.nusl.cz/ntk/nusl-77061.
Full textKondapalli, Swetha. "An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1596491788206805.
Full textGunay, Melih. "Representation Of Covariance Matrices In Track Fusion Problems." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609026/index.pdf.
Full textAbbasian, Houman. "Inner Ensembles: Using Ensemble Methods in Learning Step." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31127.
Full textSarazin, Marianne. "Elaboration d'un score de vieillissement : propositions théoriques." Phd thesis, Université Jean Monnet - Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-00994941.
Full textRamler, Ivan Peter. "Improved statistical methods for k-means clustering of noisy and directional data." [Ames, Iowa : Iowa State University], 2008.
Find full textBooks on the topic "Method of k-means"
Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.
Full textBaym, Nancy K. Playing to the Crowd. NYU Press, 2018. http://dx.doi.org/10.18574/nyu/9781479896165.001.0001.
Full textSkiba, Grzegorz. Fizjologiczne, żywieniowe i genetyczne uwarunkowania właściwości kości rosnących świń. The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, 2020. http://dx.doi.org/10.22358/mono_gs_2020.
Full textBook chapters on the topic "Method of k-means"
Prasad, Rabinder Kumar, Rosy Sarmah, and Subrata Chakraborty. "Incremental k-Means Method." In Lecture Notes in Computer Science, 38–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34869-4_5.
Full textPadmavathi, S., C. Rajalaxmi, and K. P. Soman. "Texel Identification Using K-Means Clustering Method." In Advances in Intelligent Systems and Computing, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30111-7_27.
Full textZi, Ye, Liang Kun, Zhiyuan Zhang, Chunfeng Wang, and Zhe Peng. "An Improved Bisecting K-Means Text Clustering Method." In Advances in Intelligent Systems and Computing, 155–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34387-3_19.
Full textTian, Shengwen, Hongyong Yang, Yilei Wang, and Ali Li. "An Improved K-Means Clustering Algorithm Based on Spectral Method." In Advances in Computation and Intelligence, 530–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92137-0_58.
Full textBaruri, Rajdeep, Anannya Ghosh, Saikat Chanda, Ranjan Banerjee, Anindya Das, Arindam Mandal, and Tapas Halder. "A Comparative Study on k-means Clustering Method and Analysis." In Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics, 113–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8300-7_10.
Full textFujimoto, Kyoko, Leonardo M. Angelone, Sunder S. Rajan, and Maria Ida Iacono. "Simplifying the Numerical Human Model with k-means Clustering Method." In Brain and Human Body Modeling 2020, 261–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_15.
Full textAlmajidi, Abdo Mahyoub, V. P. Pawar, and Abdulsalam Alammari. "K-Means-Based Method for Clustering and Validating Wireless Sensor Network." In International Conference on Innovative Computing and Communications, 251–58. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2324-9_25.
Full textVan, Thanh The, Nguyen Van Thinh, and Thanh Manh Le. "The Method Proposal of Image Retrieval Based on K-Means Algorithm." In Advances in Intelligent Systems and Computing, 481–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_45.
Full textGu, Lei. "A Locality Sensitive K-Means Clustering Method Based on Genetic Algorithms." In Lecture Notes in Computer Science, 114–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38715-9_14.
Full textSarma, T. Hitendra, and P. Viswanath. "Speeding-Up the K-Means Clustering Method: A Prototype Based Approach." In Lecture Notes in Computer Science, 56–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11164-8_10.
Full textConference papers on the topic "Method of k-means"
Liu, Dongju, and Jian Yu. "Otsu Method and K-means." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.74.
Full textCabria, Ivan, and Iker Gondra. "A Mean Shift-Based Initialization Method for K-means." In 2012 IEEE 12th International Conference on Computer and Information Technology (CIT). IEEE, 2012. http://dx.doi.org/10.1109/cit.2012.124.
Full textShao, Xiuli, Huichao Lee, Yiwei Liu, and Bo Shen. "Automatic K selection method for the K — Means algorithm." In 2017 4th International Conference on Systems and Informatics (ICSAI). IEEE, 2017. http://dx.doi.org/10.1109/icsai.2017.8248533.
Full textWahyuningrum, Tenia, Siti Khomsah, Suyanto Suyanto, Selly Meliana, Prasti Eko Yunanto, and Wikky F. Al Maki. "Improving Clustering Method Performance Using K-Means, Mini Batch K-Means, BIRCH and Spectral." In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2021. http://dx.doi.org/10.1109/isriti54043.2021.9702823.
Full textZhang, Peng, Lingling Zhang, Guangli Nie, Yuejin Zhang, and Yong Shi. "Transfer Knowledge via Relational K-Means Method." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.153.
Full textCui, Xiaowei, and Fuxiang Wang. "An Improved Method for K-Means Clustering." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.154.
Full textArthur, David, and Sergei Vassilvitskii. "How slow is the k-means method?" In the twenty-second annual symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1137856.1137880.
Full textLiu Liu, Baosheng Wang, Qiuxi Zhong, and Hao Zeng. "A selective ensemble method based on K-means method." In 2015 4th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2015. http://dx.doi.org/10.1109/iccsnt.2015.7490832.
Full textLin, Yujun, Ting Luo, Sheng Yao, Kaikai Mo, Tingting Xu, and Caiming Zhong. "An improved clustering method based on k-means." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234296.
Full textSun, Ying, Yan Wang, Juexin Wang, Wei Du, and Chunguang Zhou. "A Novel SVC Method Based on K-means." In 2008 Second International Conference on Future Generation Communication and Networking (FGCN). IEEE, 2008. http://dx.doi.org/10.1109/fgcn.2008.203.
Full textReports on the topic "Method of k-means"
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.
Full textHansen, Peter J., and Amir Arav. Embryo transfer as a tool for improving fertility of heat-stressed dairy cattle. United States Department of Agriculture, September 2007. http://dx.doi.org/10.32747/2007.7587730.bard.
Full textMultiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.
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