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Journal articles on the topic 'X-Means'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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1

Adams, Fred, and Kenneth Aizawa. "“X” means X: Semantics Fodor-style." Minds and Machines 2, no. 2 (1992): 175–83. http://dx.doi.org/10.1007/bf00704456.

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2

Han, Myoungji, Jihyuk Lim, Junyong Choi, et al. "A Malicious Traffic Detection Method Using X-means Clustering." Journal of KIISE 41, no. 9 (2014): 617–24. http://dx.doi.org/10.5626/jok.2014.41.9.617.

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3

Abdelrahman, Radwan, Abdellatif Nasser, Radwan Eyad, and Akhozahieh Maryam. "Fitness function X-means for prolonging wireless sensor networks lifetime." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 465–72. https://doi.org/10.11591/ijece.v13i1.pp465-472.

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X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (j) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to determine the acceptable number of children. This article proposes fitness function X-means (FFX-means) as an enhance

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4

Larasati, Aisyah, Apif Miftahul Hajji, Anik Nur Handayani, Nabila Azzahra, Muhammad Farhan, and Puji Rahmawati. "Profiling Academic Library Patrons using K-means and X-means Clustering." International Journal of Technology 10, no. 8 (2019): 1567. http://dx.doi.org/10.14716/ijtech.v10i8.3440.

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5

HO, T. L. "Petrofabric Analysis by Means of X-Ray1." Bulletin of the Geological Society of China 27, no. 1 (2009): 389–98. http://dx.doi.org/10.1111/j.1755-6724.1947.mp27001027.x.

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6

Krutzenbichler, A., J. Keimel, and G. Fritsch. "Locally Resolved X-Ray Investigations by Means of X-Ray Optics." Materials Science Forum 133-136 (January 1993): 243–48. http://dx.doi.org/10.4028/www.scientific.net/msf.133-136.243.

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7

Rokhmatan Khaerullah, Rizal, Nana Suarna, and Odi Nurdiawan. "Analisa Pengelompokan Dataset Komputer Menggunakan Algoritma X-Means." Jurnal Informatika dan Teknologi Informasi 1, no. 3 (2023): 125–31. http://dx.doi.org/10.56854/jt.v1i3.135.

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Computer dataset cluster analysis is a data mining technique that aims to identify a group of objects that have the same characteristics. The problem in this research is how to implement the X-Means Clustering algorithm in grouping computer datasets based on computer prices and specifications. The purpose of this study is to determine the implementation of the X-Means Clustering method in classifying computer datasets against prices and specifications. This computer dataset consists of several attributes with a large number of records so we need a process that can group the data, namely cluste

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Cazaux, J. "Characteristic x-ray imaging by means of filters." X-Ray Spectrometry 28, no. 1 (1999): 9–18. http://dx.doi.org/10.1002/(sici)1097-4539(199901/02)28:1<9::aid-xrs299>3.0.co;2-l.

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9

Bustami, Bustami, Rike Mahara, Hendri Ahmadian, Sri Wahyuni, and Khairan AR. "Analisis Clustering Penduduk Miskin Di Provinsi Aceh Menggunakan Algoritma K-Means Dan X-Means." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 5, no. 1 (2022): 26–35. http://dx.doi.org/10.32672/jnkti.v5i1.3961.

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Abstrak-Analisis Clustering merupakan salah satu metode yang bertujuan untuk mengelompokan data/objek berdasarkan kemiripan dan ketidakmiripan karakteristiknya. Sehingga objek yang berada pada satu cluster memiliki kemiripan yang besar dan sangat kecil bila dibandingkan dengan cluster lain. Algoritma K-means merupakan salah satu metode pengelompokan non hierarki yang paling umum digunakan, namun pada algoritma ini pengguna harus mengetahui jumlah kelompok yang akan dikelompokan. Sedangkan algoritma X-means merupakan pengembangan dari algoritma K-means, dimana algoritma ini mampu mengelompokan

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10

Kaligis, Gideon Bartolomeus, and Sri Yulianto. "ANALISA PERBANDINGAN ALGORITMA K-MEANS, K-MEDOIDS, DAN X-MEANS UNTUK PENGELOMPOKKAN KINERJA PEGAWAI." IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi 1, no. 3 (2022): 179–93. http://dx.doi.org/10.24246/itexplore.v1i3.2022.pp179-193.

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Kinerja pegawai menjadi rangkuman dalam hal kualitas, kuantitas, jam kerja dan juga kerja sama untuk mencapai suatu tujuan yang telah ditetapkan oleh instansi atau perusahaan, namun dalam Sekretariat DPRD Provinsi Sulawesi Utara belum adanya metode untuk menentukan pengelompokkan kinerja pegawai. Untuk mengatasi permasalahan ini, diperlukan adanya pengelompokan kinerja pegawai di Sekretariat DPRD Provinsi Sulawesi Utara, sehingga bisa menentukan kinerja pegawai yang berkualitas. Tujuan dari penelitian ini dengan melakukan perbandingan metode-metode clustering untuk mendapatkan metode yang lebi

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11

Wahidin, Ahmad Jurnaidi, and Dana Indra Sensuse. "Perbandingan Algoritma K-Means, X-Means Dan K-Medoids Untuk Klasterisasi Awak Kabin Lion Air." Jurnal ICT : Information Communication & Technology 20, no. 2 (2021): 298–302. http://dx.doi.org/10.36054/jict-ikmi.v20i2.387.

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Lion Air is part of PT. Lion Group, which is a airline in Indonesia and a low-cost airline based in Jakarta, Lion Air throughout the year experienced an increase in its fleet and an increase in the number of flights, the greater the need for cabin crew, in addition to the recruitment process which must be selective, it is also necessary to monitor the crew. cabin crew so that cabin crew performance will continue to be well maintained so that several groups are formed called cabin crew monitoring groups, currently cabin crew clustering is carried out randomly so as to produce group members with

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12

Gunnelin, K., P. Glans, P. Skytt, J. H. Guo, J. Nordgren, and H. Ågren. "Assigning x-ray absorption spectra by means of soft-x-ray emission spectroscopy." Physical Review A 57, no. 2 (1998): 864–72. http://dx.doi.org/10.1103/physreva.57.864.

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13

Radwan, Abdelrahman, Nasser Abdellatif, Eyad Radwan, and Maryam Akhozahieh. "Fitness function X-means for prolonging wireless sensor networks lifetime." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 465. http://dx.doi.org/10.11591/ijece.v13i1.pp465-472.

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<span lang="EN-US">X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (</span><em><span lang="EN-US">j</span></em><span lang="EN-US">) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to d

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14

Rohendi, Ghina Fitria, Nana Suarna, and Gifthera Dwi Lestari. "Analisis Rumah Tidak Layak Huni Menggunakan Algoritma X-Means." Jurnal Janitra Informatika dan Sistem Informasi 3, no. 1 (2023): 18–29. http://dx.doi.org/10.25008/janitra.v3i1.151.

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Tempat tinggal atau rumah merupakan kebutuhan masyarakat yang sangat mendasar bagi setiap orang selain sandang dan pangan. Rumah yang layak huni dapat diketahui dari struktur bangunan rumah dan fasilitas rumah yang ada. Struktur bangunan rumah meliputi luas lantai, jenis lantai, jenis atap, dan jenis dinding. Sedangkan fasilitas rumah meliputi sumber air minum, sumber penerangan, dan tempat pembuangan akhir kotoran. Dalam penelitian ini penulis akan melakukan pengelompokan rumah tidak layak huni dengan melihat struktur bangunan rumah dan fasilitas rumah yang dimiliki setiap rumah tangga di 4 w

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15

ZUHRUFILLAH, IRFANI. "Pengelompokkan Jenis Minyak Mentah Menggunakan Metode X-Means Clustering." Journal of Informatics, Information System, Software Engineering and Applications (INISTA) 4, no. 1 (2021): 12–21. http://dx.doi.org/10.20895/inista.v4i1.377.

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The price of crude oil in Indonesia is still very volatile it cause of strongly influenced by the demand and supply mechanism. The other factor is the fundamental factor, the non-fundamental factor, and the influence and policy of OPEC factor. Refined petroleum is indispensable in life, used for various human purposes for fuels such as gasoline and kerosene, asphalt, and some of the chemical reagents used to make plastics and medicines. These crude oil prices Information in Indonesia are open and officially published by the Ministry of Energy and Mineral Resources website from 2017 until now.

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16

Ebel, H., R. Svagera, and S. Rezai Afshar. "Depth Profiling by Means of X-Ray Fluorescence Analysis." Advances in X-ray Analysis 35, B (1991): 783–94. http://dx.doi.org/10.1154/s0376030800012982.

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Sherman described the excitation of characteristic radiations by primary x-rays and by secondary excitation. The derivation has been made assuming a homogeneous sample. Criss and Birks inverted the problem from the calculation of fluorescent countrates to the quantitative XFA by means of fundamental parameters. Theoretical and instrumental developments enabled a reduction of the sample area and led to small area XFA and imaging XFA sytems. Depth profiling by means of XFA is a further development. We continue the original concept of variable take-off angle technique for the determination of fil

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17

Ebel, M. F., H. Ebel, and F. Olcaytug. "Depth Profiling by Means of X-Ray Photoelectron Spectrometry." Advances in X-ray Analysis 35, B (1991): 857–67. http://dx.doi.org/10.1154/s0376030800013070.

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The depth range d of x-ray photoelectron spectrometry (XPS) is determined by the inelastic mean free path λ of photoelectrons . The following considerations are dedicated to a correlation between d and λ.

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18

Tashenov, S., A. Khaplanov, B. Cederwall, and K. U. Schässburger. "Hard X-ray polarimetry by means of Rayleigh scattering." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 600, no. 3 (2009): 599–603. http://dx.doi.org/10.1016/j.nima.2008.11.145.

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19

Zendrato, N., H. W. Dhany, N. A. Siagian, and F. Izhari. "Bigdata Clustering using X-means method with Euclidean Distance." Journal of Physics: Conference Series 1566 (June 2020): 012103. http://dx.doi.org/10.1088/1742-6596/1566/1/012103.

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20

Kayser, Yves, Jacinto Sá, and Jakub Szlachetko. "Depth-Resolved X-ray Absorption Spectroscopy by Means of Grazing Emission X-ray Fluorescence." Analytical Chemistry 87, no. 21 (2015): 10815–21. http://dx.doi.org/10.1021/acs.analchem.5b03346.

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21

Kumada, N., N. Ozawa, N. Kinomura, and F. Muto. "Preparation of Li3-x H x TaO4 by means of hydrothermal synthesis and protolysis." Journal of Materials Science 21, no. 12 (1986): 4140–42. http://dx.doi.org/10.1007/bf01106519.

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22

Li, J. "Investigation of La1−x Ca x FeO3−y (0≦x≦0.50) by means of X—ray diffraction and Mössbauer spectroscopy." Hyperfine Interactions 69, no. 1-4 (1992): 573–76. http://dx.doi.org/10.1007/bf02401892.

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23

Dubeau, Francois. "On a generalization of u-means." International Journal of Mathematics and Mathematical Sciences 14, no. 4 (1991): 803–7. http://dx.doi.org/10.1155/s0161171291001072.

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In this paper we present an extension of Bauer's work about u-means. We consider a kind of composition of an admissible function u(x) (described by Bauer) and of a compatible function ϕ(x). This construction allows us to define (u,ϕ)-means. When ϕ(x)=x, the (u,ϕ)-means are the u-means introduced by Bauer. The arithmetic, geometric and harmonic means are special cases.

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24

Adhitama, Rifki, Auliya Burhanuddin, and Ridho Ananda. "PENENTUAN JUMLAH CLUSTER IDEAL SMK DI JAWA TENGAH DENGAN METODE X-MEANS CLUSTERING DAN K-MEANS CLUSTERIN." JIKO (Jurnal Informatika dan Komputer) 3, no. 1 (2020): 1–5. http://dx.doi.org/10.33387/jiko.v3i1.1635.

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25

CAO, Peng, Bo LI, Wei LI, and Dazhe ZHAO. "Adaptive random subspace ensemble classification aided by X-means clustering." Journal of Computer Applications 33, no. 2 (2013): 550–53. http://dx.doi.org/10.3724/sp.j.1087.2013.00550.

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26

Ach. Syuhbanul Yaumi, Zainul Zulfiqkar, and Aryo Nugroho. "Sistem Penunjang Keputusan Pemasaran Produk X Menggunakan Metode K-Means." Systemic: Information System and Informatics Journal 6, no. 1 (2021): 44–50. http://dx.doi.org/10.29080/systemic.v6i1.936.

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Permasalahan yang sedang ada di toko saat ini adalah kesulitan untuk mengetahui produk X yang saat ini banyak diminati atau paling banyak digunakan oleh konsumen agar persediaan produk x dapat dipenuhi sesuai permintaan pelanggan dan tidak terjadi out of stock. Oleh karena itu, dalam penelitian pengelompokkan dengan metode K-Means untuk pemasaran produk x merupakan salah satu cara untuk mengetahui pilihan pelanggan terhadap produk x yang dikonsumsi. Pada penelitian ini melakukan pengelompokkan dari data hasil penyebaran angket atau kusioner yang disebar di toko, kemudian data tersebut di kelom

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27

SUGAWARA, Katsuhiko, Ryusuke KOJIMA, Yuzo OBARA, Akira SATO, and Hideki SHIMADA. "Crack Opening Analysis by means of the X-Rays CT." Shigen-to-Sozai 114, no. 12 (1998): 881–87. http://dx.doi.org/10.2473/shigentosozai.114.881.

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28

Golovashkin, A. I., N. D. Kuz’michev, and V. V. Slavkin. "Controllable generation of harmonics by means of YBa2Cu3O7 − x superconductors." Technical Physics 53, no. 10 (2008): 1314–18. http://dx.doi.org/10.1134/s1063784208100095.

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29

T. F. Schatzki, R. P. Haff, R. Young, I. Can, L-C. Le, and N. Toyofuku. "DEFECT DETECTION IN APPLES BY MEANS OF X-RAY IMAGING." Transactions of the ASAE 40, no. 5 (1997): 1407–15. http://dx.doi.org/10.13031/2013.21367.

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30

Kamminga, J. D., and R. Delhez. "Measurement of dislocation distributions by means of X-ray diffraction." Materials Science and Engineering: A 309-310 (July 2001): 55–59. http://dx.doi.org/10.1016/s0921-5093(00)01665-8.

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31

Anikina, E. Yu, and V. N. Verbetsky. "Investigation of ZrMn2+x-H2 by means of calorimetric method." Journal of Alloys and Compounds 446-447 (October 2007): 443–46. http://dx.doi.org/10.1016/j.jallcom.2006.12.146.

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32

Freyburger, Charline, Fleur Longuetaud, Frédéric Mothe, Thiéry Constant, and Jean-Michel Leban. "Measuring wood density by means of X-ray computer tomography." Annals of Forest Science 66, no. 8 (2009): 804. http://dx.doi.org/10.1051/forest/2009071.

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33

CNUDDE, V., B. MASSCHAELE, H. E. V. DE COCK, et al. "Virtual histology by means of high-resolution X-ray CT." Journal of Microscopy 232, no. 3 (2008): 476–85. http://dx.doi.org/10.1111/j.1365-2818.2008.02142.x.

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34

Inoue, Hirofumi. "Texture analysis by means of EBSD and X-ray diffraction." Journal of Japan Institute of Light Metals 60, no. 12 (2010): 666–75. http://dx.doi.org/10.2464/jilm.60.666.

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35

Lauer, V., F. Bauer, J. Korec, H. L. Huber, and P. Balk. "Fabrication of halfmicron MOSFETs by means of x-ray lithography." Microelectronic Engineering 6, no. 1-4 (1987): 215–20. http://dx.doi.org/10.1016/0167-9317(87)90040-2.

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36

Langenberg, A., F. Warmer, G. Fuchert, et al. "Impurity transport studies at Wendelstein 7-X by means of x-ray imaging spectrometer measurements." Plasma Physics and Controlled Fusion 61, no. 1 (2018): 014030. http://dx.doi.org/10.1088/1361-6587/aaeb74.

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37

Betz, H. "Resolution limits in x-ray lithography calculated by means of x-ray lithography simulator XMAS." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 4, no. 1 (1986): 248. http://dx.doi.org/10.1116/1.583450.

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38

Blagov, A. E., M. V. Kovalchuk, A. V. Targonsky, and Y. V. Pisarevsky. "A new method for measuring X-ray rocking curves by means of X-ray acoustooptics." Acta Crystallographica Section A Foundations of Crystallography 67, a1 (2011): C325—C326. http://dx.doi.org/10.1107/s0108767311091823.

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39

Bouillot, J., J. L. Soubeyroux, and F. Luty. "(KCN)x(KCl)1−x: Structural phase diagram revisited by means of neutron thermo-diffractometry." Physica B: Condensed Matter 156-157 (January 1989): 81–84. http://dx.doi.org/10.1016/0921-4526(89)90593-0.

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40

Hafizah, Izatul, and Kiki Vierdayanti. "Probing Supercritical Accretion in Ultraluminous X-ray Source M82 X-1 by means of X-ray Spectral Evolution Analysis." EPJ Web of Conferences 240 (2020): 07005. http://dx.doi.org/10.1051/epjconf/202024007005.

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We analyze the spectral evolution of ultraluminous X-ray source (ULX) M82 X-1 by means of spectral fitting. We use selected Swift/XRT data in 2014 and 2015. The flux of M82 X-1 increased by a factor of 2-3 from 2014 to 2015. Most of the data in 2015 show greater dominance of hard component than those of 2014. Due to moderate signal-to-noise ratio, we only fit each spectrum with power-law and disk blackbody model separately. The data in 2014 are better fitted with powerlaw model based on the value of reduced-chi squared. On the other hand, both powerlaw and diskbb models showed comparable re- d

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41

Ariza, Disya Nurul, Rahayu Ningsih, Sri Muryani, Herlina Ferliyanti, and Ahmad Jurnaidi Wahidin. "COMPARATIVE ANALYSIS OF K-MEANS, X-MEANS AND K-MEDOIDS IN CLASSIFYING MARRIAGE CHOICED ADMIST QUARTER-LIFE CRISIS." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 1 (2024): 69–76. https://doi.org/10.33330/jurteksi.v11i1.3554.

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Abstract: Bekasi Regency, being one of the key cities in Indonesia, offers a suitable setting to study the intricacies of marriage decision-making during a quarter-life crisis. This study focuses on the application of clustering algorithms to categorize individuals based on their marriage choices. Data was collected from a questionnaire completed by 110 respondents from Bekasi Regency, specifically individuals aged 18 to 30 who are single, including 80 women and 30 men. Data analysis was conducted using the RapidMiner software to evaluate the effectiveness of three clustering algorithms K-Mean

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42

Tahalea, Syilvert, Rahmi Fadhilah, M. Y. Matdoan, and Dinda Ayu Safira. "CLUSTERING SHRIMP DISTRIBUTION IN INDONESIA USING THE X-MEANS CLUSTERING ALGORITHM." VARIANCE: Journal of Statistics and Its Applications 6, no. 1 (2024): 49–54. http://dx.doi.org/10.30598/variancevol6iss1page49-54.

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Shrimp is one of the marine biological resources available in almost all Indonesian waters and is one of the mainstay export commodities from the fisheries sub-sector. This is expected to improve the welfare of the community, so it is necessary to cluster the distribution of shrimp in Indonesia. Clustering is a data mining technique used to group data or partition datasets into subsets. One of the best clustering algorithms is X-means. X-means clustering is used to solve one of the main disadvantages of K-means clustering, namely the need for prior knowledge of the number of clusters (K). The

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43

Pasteczka, Paweł. "Interval-type theorems concerning means." Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica 17, no. 1 (2018): 37–43. http://dx.doi.org/10.2478/aupcsm-2018-0004.

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Abstract Each family ℳ of means has a natural, partial order (point-wise order), that is M ≤ N iff M(x) ≤ N(x) for all admissible x. In this setting we can introduce the notion of interval-type set (a subset ℐ ⊂ℳ such that whenever M ≤ P ≤ N for some M, N ∈ℐ and P ∈ℳ then P ∈ℐ). For example, in the case of power means there exists a natural isomorphism between interval-type sets and intervals contained in real numbers. Nevertheless there appear a number of interesting objects for a families which cannot be linearly ordered. In the present paper we consider this property for Gini means and Hard

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44

Tanti, Tanti, and Fadhil M Basysyar. "IMPLEMENTASI ALGORITMA X-MEANS UNTUK MENGELOMPOKAN PENJUALAN BARANG DI CILEDUG STORE." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 2 (2024): 1608–13. http://dx.doi.org/10.36040/jati.v8i2.9026.

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Dalam menghadapi era mobilitas yang semakin berfokus pada keberlanjutan, kendaraan listrik, khususnya sepeda listrik, muncul sebagai solusi inovatif untuk mendukung masa depan transportasi yang ramah lingkungan. Perusahaan Ciledug Store memiliki kebutuhan untuk memahami pola penjualan sepeda listrik agar dapat mengoptimalkan strategi pemasaran dan persediaan. Oleh karena itu, penelitian ini bertujuan untuk mengelompokkan penjualan barang menggunakan algoritma X-Means, dengan fokus pada kendaraan listrik. Penelitian ini bertujuan untuk menerapkan algoritma X-Means dalam mengelompokkan penjualan

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45

Hidayat, Taofik, and Bramantiyo Eko Putro. "Analisis Karakteristik Konsumen Hotel “X” dengan Menggunakan Metode K-Means Clustering." Jurnal Media Teknik dan Sistem Industri 4, no. 2 (2020): 53. http://dx.doi.org/10.35194/jmtsi.v4i2.995.

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Abstract— Hotel "X" has a problem with a marketing strategy that is incompatible with the characteristics of consumers staying at the hotel "X". This is because the hotel "X" does not yet know the characteristics of its consumers. The marketing strategy that is not optimal also affects the occupancy rate of the "X" hotel. This problem can be seen from the fluctuating number of overnight stayers. So that the number of hotel visitors is small. The purpose of this study is to determine the marketing strategy that is following the characteristics of consumers in the hotel "X", using the K-means cl

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46

Friedrich, Ursula, Mette Warburg, and Arne Lund J�rgensen. "X-inactivation pattern in carriers of X-linked retinitis pigmentosa: A valuable means of prognostic evaluation?" Human Genetics 92, no. 4 (1993): 359–63. http://dx.doi.org/10.1007/bf01247335.

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47

Valcheva, E. P. "Characterization of GaAs/Al x Ga 1-x As heterointerface defects by means of capacitive measurememts." Applied Physics A: Materials Science & Processing 65, no. 1 (1997): 39–42. http://dx.doi.org/10.1007/s003390050538.

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