Relevant bibliographies by topics / K-Means Technique

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'K-Means Technique'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "K-Means Technique"

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Mohamed, Rozlini, Munirah Mohd Yusof, Noorhaniza Wahid, Norhanifah Murli, and Muhaini Othman. "Bat algorithm and k-means techniques for classification performance improvement." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1411. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1411-1418.

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This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Sec
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Nelson, Butarbutar, Perdana Windarto Agus, Hartama Dedy, and Solikhun. "KOMPARASI KINERJA ALGORITMA FUZZY C-MEANS DAN K-MEANS DALAM PENGELOMPOKAN DATA SISWA BERDASARKAN PRESTASI NILAIAKADEMIK SISWA." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2016): 46–55. https://doi.org/10.5281/zenodo.546761.

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Various efforts have been undertaken by schools to improve academic achievement students in an effort to achieve national education standards. One of which is by doing tutoring for each student, but the results have not been so satisfying. This is due to the particular school education section did not fully understand each student's ability to master an eye teaching core subjects, especially the UN. To overcome this by utilizing clustering techniques will do the data grouping students based on merit value academic sources of data obtained directly from the education department. With use cluste
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Ali, Mariam A. "IMAGE GENERATION BY USING K-MEANS CLUSTERING TECHNIQUE." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 03 (2019): 3092–96. https://doi.org/10.5281/zenodo.14823018.

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This paper presents a new approach for image generation by applying k-means algorithm. The K-means clustering algorithm is one of the most widely used algorithm in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means. Our research proposes a color-based image generation method that uses K-means clustering technique which is an iterative technique used to partition an image into k clusters. At first the pixels of source image are clustered into k partitions based on their color, where the clustering process is accomplished. Then the c
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K.Sarode, Tanuja, and Nabanita Mandal. "K-Means Codebook Optimization using KFCG Clustering Technique." International Journal of Computer Applications 78, no. 6 (2013): 38–43. http://dx.doi.org/10.5120/13496-1228.

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Haviluddin, Haviluddin, Yudi Sukmono, Didit Suprihanto, Arif Harjanto, and Olivia Angelica Murtioso. "Drugs Consumption Clustering Model using K-Means Technique." CESS (Journal of Computer Engineering, System and Science) 7, no. 2 (2022): 537. http://dx.doi.org/10.24114/cess.v7i2.36104.

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Persediaan obat pada suatu Puskesmas seringkali habis sebelum jadwal penerimaan obat dilakukan hal ini dikarenakan Kejadian Luar Biasa (KLB). Sehingga, perencanaan persediaan obat yang efektif dan efisien dengan menerapkan metode kecerdasan buatan dalam rangka membantu pihak manajemen sangat diperlukan. Penelitian ini bertujuan untuk memonitoring persediaan obat sebagai salah satu dasar dalam permintaan obat. Data pemakaian obat yang digunakan berasal dari Laporan Pemakaian dan Lembar Permintaan Obat (LPLPO) UPTD Puskesmas Lempake tahun 2016-2018 dan telah dinormalisasi dengan metode Z-Score.
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Chauhan, Alok Singh. "Healthcare decision making using k-means clustering technique." Pranjana:The Journal of Management Awareness 23, no. 2 (2020): 28–33. http://dx.doi.org/10.5958/0974-0945.2020.00009.6.

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G.Ravindran1, T.Joby Titus2* V.Ganesh3 V.S.Sanjana Devi4. "ANALYSIS OF IMAGE SEGMENTATION TECHNIQUES FOR TEXTURE FEATURE EXTRACTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 66–71. https://doi.org/10.5281/zenodo.802824.

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The pixels of an image are grouped into several regions for segmentation. In segmentation technique the texture feature parameter is an image analysis technique in the field of Computer vision. In the Segmentation field, there are many techniques are used to segment the images .The proposed approach is to analyze and compare the gray level texture feature techniques, number of clusters, Fuzzy C means, and to find which algorithmic approach provides better results in image segmentation.
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Hendra Wijaya. "Analysis of Deep K-Means and K-Means for Journal Summarization Using the BERTopic Method." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 715–24. https://doi.org/10.52783/jisem.v10i43s.8470.

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This research aims to analyze the performance of Deep K-means and K-means methods in summarizing and clustering final project abstracts using the BERTopic technique. The methods used include preprocessing abstract data, applying Deep K-means and K-means clustering algorithms, and modeling topics with BERTopic. The dataset used was obtained from Kaggle and analyzed using the Python programming language. The results show that the Deep K-means method is able to produce more coherent topic clusters than conventional K-Means, with higher Silhouette Score values. The combination of clustering and to
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Pham, D. T., S. S. Dimov, and C. D. Nguyen. "An Incremental K-means algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 7 (2004): 783–95. http://dx.doi.org/10.1243/0954406041319509.

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Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency. The main problem with this clustering method is its tendency to coverge at a local minimum. In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined. The jumping technique alleviates the problem with local
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Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm." International Journal of Computer Vision and Image Processing 8, no. 4 (2018): 16–25. http://dx.doi.org/10.4018/ijcvip.2018100102.

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The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hie
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Dissertations / Theses on the topic "K-Means Technique"

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Thirathon, Nattavude 1980. "Cyclic exchange neighborhood search technique for the K-means clustering problem." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17981.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 151-152).<br>Cyclic Exchange is an application of the cyclic transfers neighborhood search technique for the k-means clustering problem. Neighbors of a feasible solution are obtained by moving points between clusters in a cycle. This method attempts to improve local minima obtained by the well-known Lloyd's algorithm. Although the results did not establish usefulness of Cyclic Exchange, our experiments reveal some insights on the k-mean
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Ranby, Erik. "A comparison of clustering techniques for short social text messages." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196735.

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The amount of social text messages authored each day is huge and the information contained within is potentially very valuable. Software that can cluster and thereby help analyze these messages would consequently be helpful. This thesis explores several ways of clustering social text messages. Two algorithms and several setups with these algorithms have been tested and evaluated with the same data as input. Based on these evaluations, a comparison has been conducted in order to answer the question which algorithm setup is best suited for the task. The two clustering algorithms that have been t
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Kerwin, Matthew. "Comparison of Traditional Image Segmentation Techniques and Geostatistical Threshold." Thesis, James Cook University, 2006. https://eprints.qut.edu.au/99764/1/kerwin-honours-thesis.pdf.

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A general introduction to image segmentation is provided, including a detailed description of common classic techniques: Otsu’s threshold, <i>k</i>-means and fuzzy <i>c</i>-means clustering; and suggestions of ways in which these techniques have been subsequently modified for special situations. Additionally, a relatively new approach is described, which attempts to address certain exposed failings of the classic techniques listed by incorporating a spatial statistical analysis technique commonly used in geological studies. Results of different segmentation techniques are calculated for
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Abreu, Rodolfo Telo Martins de. "Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8250.

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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica<br>One of the biggest challenges when analysing data is to extract information from it, especially if we dealing with very large sized data, which brings a new set of barriers to be overcome. The extracted information can be used to aid physicians in their diagnosis since biosignals often carry vital information on the subjects. In this research work, we present a signal-independent algorithm with two main goals: perform events detection in biosignals and, with those events, extract information using a set of distance meas
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Frederic, John. "Examination of Initialization Techniques for Nonnegative Matrix Factorization." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/63.

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While much research has been done regarding different Nonnegative Matrix Factorization (NMF) algorithms, less time has been spent looking at initialization techniques. In this thesis, four different initializations are considered. After a brief discussion of NMF, the four initializations are described and each one is independently examined, followed by a comparison of the techniques. Next, each initialization's performance is investigated with respect to the changes in the size of the data set. Finally, a method by which smaller data sets may be used to determine how to treat larger data sets
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Aksoy, Ece. "An Attempt To Classify Turkish District Data: K-means And Self-organizing Map (som) Algorithms." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605711/index.pdf.

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ABSTRACT AN ATTEMPT TO CLASSIFY TURKISH DISTRICT DATA: K-MEANS AND SELF-ORGANIZING MAP (SOM) ALGORITHMS Aksoy, Ece M.S., Department of Geodetic and Geographic Information Systems Supervisor: Assoc. Prof. Dr. Oguz ISik December 2004, 112 pages There is no universally applicable clustering technique in discovering the variety of structures display in data sets. Also, a single algorithm or approach is not adequate to solve every clustering problem. There are many methods available, the criteria used differ and hence different classifications may be obtained for the same data. While large
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Arnroth, Lukas, and Dennis Jonni Fiddler. "Supervised Learning Techniques : A comparison of the Random Forest and the Support Vector Machine." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274768.

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This thesis examines the performance of the support vector machine and the random forest models in the context of binary classification. The two techniques are compared and the outstanding one is used to construct a final parsimonious model. The data set consists of 33 observations and 89 biomarkers as features with no known dependent variable. The dependent variable is generated through k-means clustering, with a predefined final solution of two clusters. The training of the algorithms is performed using five-fold cross-validation repeated twenty times. The outcome of the training process rev
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Örnbratt, Filip, Jonathan Isaksson, and Mario Willing. "A comparative study of social bot classification techniques." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16994.

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With social media rising in popularity over the recent years, new so called social bots are infiltrating by spamming and manipulating people all over the world. Many different methods have been presented to solve this problem with varying success. This study aims to compare some of these methods, on a dataset of Twitter account metadata, to provide helpful information to companies when deciding how to solve this problem. Two machine learning algorithms and a human survey will be compared on the ability to classify accounts. The algorithms used are the supervised algorithm random forest and the
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Mansouri, Abdelkhalek. "Generic heuristics on GPU to superpixel segmentation and application to optical flow estimation." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA012.

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Déterminer des clusters dans des nuages de points et apparier des graphes sont des tâches primordiales en informatique, analyse de donnée, traitement d’image, généralement modélisées par des problèmes d’optimisation de classe NP-difficile. Avec l’avènement des multiprocesseurs à bas coût, l’accélération des procédures heuristiques pour ces tâches devient possible et nécessaire. Nous proposons des implantations parallèles sur système GPU (graphics processing unit) pour des algorithmes génériques appliqués ici à la segmentation d’image en superpixels et au problème du flot optique. Le but est de
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Storer, Jeremy J. "Computational Intelligence and Data Mining Techniques Using the Fire Data Set." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796.

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Books on the topic "K-Means Technique"

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Skiba, 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.

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Bones are multifunctional passive organs of movement that supports soft tissue and directly attached muscles. They also protect internal organs and are a reserve of calcium, phosphorus and magnesium. Each bone is covered with periosteum, and the adjacent bone surfaces are covered by articular cartilage. Histologically, the bone is an organ composed of many different tissues. The main component is bone tissue (cortical and spongy) composed of a set of bone cells and intercellular substance (mineral and organic), it also contains fat, hematopoietic (bone marrow) and cartilaginous tissue. Bones a
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Book chapters on the topic "K-Means Technique"

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Sahoo, Giridhari, and Sony Snigdha Sahoo. "Accounting Fraud Detection Using K-Means Clustering Technique." In Machine Learning and Information Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2_17.

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Vaibhavi, Patel, and Kapdi Rupal. "Brain Tumor Segmentation Using K-means–FCM Hybrid Technique." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7386-1_30.

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Mosharrat, Nazifa, Iqbal H. Sarker, Md Musfique Anwar, Muhammad Nazrul Islam, Paul Watters, and Mohammad Hammoudeh. "Automatic Malware Categorization Based on K-Means Clustering Technique." In Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6636-0_49.

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Zhu, Bin, Yulan Li, Zhi Li, et al. "K-Means Based Multi-dimensional Charging Behavior Profiling Technique." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3408-9_56.

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Sarvani, A., B. Venugopal, and Nagaraju Devarakonda. "Anomaly Detection Using K-means Approach and Outliers Detection Technique." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0589-4_35.

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Roy, Kaushiki, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri, and Pramit Ghosh. "A Novel Technique for Segmenting Platelets by k-Means Clustering." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5427-3_3.

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Chitralegha, M., and K. Thangavel. "Bisecting K-Means Initialization Technique for Protein Sequence Motif Identification." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_14.

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Sahoo, Swagatika. "A Parallel Forecasting Approach Using Incremental K-means Clustering Technique." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3874-7_16.

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Sarvani, A., B. Venugopal, and Nagaraju Devarakonda. "A Refined K-Means Technique to Find the Frequent Item Sets." In Cognitive Science and Artificial Intelligence. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6698-6_5.

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Kumar, V. Saravana, E. R. Naganathan, S. Anantha Sivaprakasam, and M. Kavitha. "Robust K-Means Technique for Band Reduction of Hyperspectral Image Segmentation." In Applications of Hybrid Metaheuristic Algorithms for Image Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40977-7_4.

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Conference papers on the topic "K-Means Technique"

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Aldossari, Saud Alhajaj. "K - Means Artificial Intelligence Clustering Technique for 6G Propagation Channels." In 2024 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS). IEEE, 2024. https://doi.org/10.1109/iccns62192.2024.10776258.

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Theu, Luong Thi, Tran Quang-Huy, Viet Bac T. Phung, and Tran Duc-Tan. "Integration of Median Filter and K-means Clustering Technique for Ultrasound Tomography." In 2024 Tenth International Conference on Communications and Electronics (ICCE). IEEE, 2024. http://dx.doi.org/10.1109/icce62051.2024.10634699.

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Albtosh, Luay, Marwan Omar, Jamal N. Al-Karaki, Derek Mohammed, and Hewa Majeed Zangana. "RobustEncoder: Leveraging K-Means clustering technique to defend NLP models against backdoor attacks." In 2024 6th International Conference on Blockchain Computing and Applications (BCCA). IEEE, 2024. https://doi.org/10.1109/bcca62388.2024.10844395.

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Agarwal, Shalove, Shashank Yadav, and Kanchan Singh. "Notice of Violation of IEEE Publication Principles - K-means versus k-means ++ clustering technique." In 2012 Students Conference on Engineering and Systems (SCES 2012). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199061.

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Bharathi, B. Sneha, and K. Veera Swamy. "Effective Image Segmentation using Modified K-Means Technique." In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2020. http://dx.doi.org/10.1109/icoei48184.2020.9142910.

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Mondal, Md Ashifuddin, and Zeenat Rehena. "Identifying Traffic Congestion Pattern using K-means Clustering Technique." In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE, 2019. http://dx.doi.org/10.1109/iot-siu.2019.8777729.

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Bishnu, P. S., and V. Bhattacherjee. "A dimension reduction technique for K-Means clustering algorithm." In 2012 1st International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2012. http://dx.doi.org/10.1109/rait.2012.6194616.

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Agrawal, Shubham, Sumit Nigam, and Kapil Sahu. "Prediction of Students Academic Execution Using K-Means and K-Medoids Clustering Technique." In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018. http://dx.doi.org/10.1109/icoei.2018.8553747.

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Baruri, Rajdeep, Anannya Ghosh, Ranjan Banerjee, Anindya Das, Arindam Mandal, and Tapas Halder. "An Empirical Evaluation of k-Means Clustering Technique and Comparison." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862215.

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Radha, R., and P. Rajendiran. "Using K-Means Clustering Technique to Study of Breast Cancer." In 2014 World Congress on Computing and Communication Technologies (WCCCT). IEEE, 2014. http://dx.doi.org/10.1109/wccct.2014.64.

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Reports on the topic "K-Means Technique"

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Oskolkov, Nikolay. Machine Learning for Computational Biology. Instats Inc., 2024. http://dx.doi.org/10.61700/l01vi14ohm8en1490.

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This one-day workshop, led by Nikolay Oskolkov from Lund University, provides a comprehensive introduction to machine learning techniques in computational biology, focusing on both theoretical knowledge and practical coding skills in R and Python. Participants will learn to implement from scratch and optimize algorithms such as neural networks, random forest, k-means clustering, and Markov Chain Monte Carlo (MCMC), making it an essential resource for advancing research in biostatistics, genetics, and data science.
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