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Journal articles on the topic 'Centroid-based clustering'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Romanuke, Vadim. "Random centroid initialization for improving centroid-based clustering." Decision Making: Applications in Management and Engineering 6, no. 2 (2023): 734–46. http://dx.doi.org/10.31181/dmame622023742.

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A method for improving centroid-based clustering is suggested. The improvement is built on diversification of the k-means++ initialization. The k-means++ algorithm claimed to be a better version of k-means is tested by a computational set-up, where the dataset size, the number of features, and the number of clusters are varied. The statistics obtained on the testing have shown that, in roughly 50 % of instances to cluster, k-means++ outputs worse results than k-means with random centroid initialization. The impact of the random centroid initialization solidifies as both the dataset size and th

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HU, TIANMING, JINZHI XIONG, and GENGZHONG ZHENG. "SIMILARITY-BASED COMBINATION OF MULTIPLE CLUSTERINGS." International Journal of Computational Intelligence and Applications 05, no. 03 (2005): 351–69. http://dx.doi.org/10.1142/s1469026805001660.

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Consensus clustering refers to combining multiple clusterings over a common set of objects into a single consolidated partition. After introducing the distribution-based view of partitions, we propose a series of entropy-based distance functions for comparing various partitions. Given a candidate partition set, consensus clustering is then formalized as an optimization problem of searching for a centroid partition with the smallest distance to that set. In addition to directly selecting the local centroid candidate, we also present two combining methods for the global centroid based on the new

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Sarmiento, Auxiliadora, Irene Fondón, Iván Durán-Díaz та Sergio Cruces. "Centroid-Based Clustering with αβ-Divergences". Entropy 21, № 2 (2019): 196. http://dx.doi.org/10.3390/e21020196.

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Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard k-means algorithm. In this article, we consider the problem of centroid-based clustering using the family of α β -divergences, which is governed by two parameters, α and β . We propose a new iterative algorithm, α β -k-means, giving closed-form solutions for the computation o

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Sim, Kelvin, Ghim-Eng Yap, David R. Hardoon, Vivekanand Gopalkrishnan, Gao Cong, and Suryani Lukman. "Centroid-Based Actionable 3D Subspace Clustering." IEEE Transactions on Knowledge and Data Engineering 25, no. 6 (2013): 1213–26. http://dx.doi.org/10.1109/tkde.2012.37.

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Liu, Rui, Zhiwei Yang, Qidong Chen, Guisheng Liao, and Weimin Zhen. "GNSS Multi-Interference Source Centroid Location Based on Clustering Centroid Convergence." IEEE Access 9 (2021): 108452–65. http://dx.doi.org/10.1109/access.2021.3101250.

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Mall, Shalu, Avinash Maurya, Ashutosh Pandey, and Davain Khajuria. "Centroid Based Clustering Approach for Extractive Text Summarization." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3404–9. http://dx.doi.org/10.22214/ijraset.2023.53542.

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Abstract: Extractive text summarization is the process of identifying the most important information from a large text and presenting it in a condensed form. One popular approach to this problem is the use of centroid-based clustering algorithms, which group together similar sentences based on their content and then select representative sentences from each cluster to form a summary. In this research, we present a centroid-based clustering algorithm for email summarization that combines the use of word embeddings with a clustering algorithm. We compare our algorithm to existing summarization t

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Azzam, Abdel Fattah, Ahmed Maghrabi, Eman El-Naqeeb, Mohammed Aldawood, and Hewayda ElGhawalby. "Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis." Applied Computational Intelligence and Soft Computing 2024 (May 22, 2024): 1–10. http://dx.doi.org/10.1155/2024/3795126.

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In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights.

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Y, Bodyanskiy, Pliss I, and Shafronenko A. "Adaptive neuro-fuzzy clustering of distorted data based on prototype-centroid strategy using evolutionary procedures." Artificial Intelligence 27, jai2022.27(1) (2022): 239–44. http://dx.doi.org/10.15407/jai2022.01.239.

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The problem of clustering is a very relevant area in Data Mining of different nature. To solve this problem, there are a large number of known methods and algorithms, most of which work in batch mode, in conditions when the entire of data set is known in advance and does not change over the time. These methods are complex in software implementa-tion and are not without drawbacks. The aim of the work is to develop an adaptive neuro-fuzzy clustering method of distorted data based on proto-type-centroid strategy using evolutionary procedures, that solves the problem in online mode, when data are

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Pandey, Kamlesh Kumar, and Diwakar Shukla. "Maxmin Data Range Heuristic-Based Initial Centroid Method of Partitional Clustering for Big Data Mining." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–22. http://dx.doi.org/10.4018/ijirr.289954.

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The centroid-based clustering algorithm depends on the number of clusters, initial centroid, distance measures, and statistical approach of central tendencies. The initial centroid initialization algorithm defines convergence speed, computing efficiency, execution time, scalability, memory utilization, and performance issues for big data clustering. Nowadays various researchers have proposed the cluster initialization techniques, where some initialization techniques reduce the number of iterations with the lowest cluster quality, and some initialization techniques increase the cluster quality

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Gullo, Francesco, and Andrea Tagarelli. "Uncertain centroid based partitional clustering of uncertain data." Proceedings of the VLDB Endowment 5, no. 7 (2012): 610–21. http://dx.doi.org/10.14778/2180912.2180914.

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11

Pietrzykowski, Marcin. "Local regression algorithms based on centroid clustering methods." Procedia Computer Science 112 (2017): 2363–71. http://dx.doi.org/10.1016/j.procs.2017.08.210.

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Antony, Jaya Mabel Rani, Srivenkateswaran C., Rajasekar M., and Arun M. "Fuzzy C-means clustering on rainfall flow optimization technique for medical data." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 180–88. https://doi.org/10.11591/ijai.v12.i1.pp180-188.

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Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clusterin

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Poonthong, Tanapon, and Jeerayut Wetweerapong. "Non-centroid-based discrete differential evolution for data clustering." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 596–605. http://dx.doi.org/10.11591/eei.v14i1.8811.

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Data clustering can find similarities and hidden patterns within data. Given a predefined number of groups, most partitional clustering algorithms use representative centers to determine their corresponding clusters. These algorithms, such as K-means and optimization-based algorithms, create and update centroids to give (hyper) spherical shape clusters. This research proposes a non-centroid-based discrete differential evolution (NCDDE) algorithm to solve clustering problems and provide non-spherical shape clusters. The algorithm directs the population of discrete vectors to search for data gro

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Li, Chunzhong, Yunong Zhang, and Xu Chen. "Heuristic Clustering Based on Centroid Learning and Cognitive Feature Capturing." Mathematical Problems in Engineering 2019 (April 8, 2019): 1–8. http://dx.doi.org/10.1155/2019/1530618.

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As one of the typical clustering algorithms, heuristic clustering is characterized by its flexibility in feature integration. This paper proposes a type of heuristic algorithm based on cognitive feature integration. The proposed algorithm employs nonparameter density estimation and maximum likelihood estimation to integrate whole and local cognitive features and finally outputs satisfying clustering results. The new approach possesses great expansibility, which enables priors supplement and misclassification adjusting during clustering process. The advantages of the new approach are as follows

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Mabel Rani, Antony Jaya, C. Srivenkateswaran, M. Rajasekar, and M. Arun. "Fuzzy C-means clustering on rainfall flow optimization technique for medical data." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 180. http://dx.doi.org/10.11591/ijai.v12.i1.pp180-188.

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<span lang="EN-US">Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an opt

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16

Rezaei, Mohammad. "Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering." Journal of Classification 37, no. 2 (2019): 352–65. http://dx.doi.org/10.1007/s00357-018-9296-4.

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17

Sun, Zhanquan, Geoffrey Fox, Weidong Gu, and Zhao Li. "A parallel clustering method combined information bottleneck theory and centroid-based clustering." Journal of Supercomputing 69, no. 1 (2014): 452–67. http://dx.doi.org/10.1007/s11227-014-1174-1.

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18

Lakshmi, R., and S. Baskar. "DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering." Journal of Information Science 45, no. 6 (2018): 818–32. http://dx.doi.org/10.1177/0165551518816302.

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In this article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering is proposed. The first centroid is the document having the minimum standard deviation of its term frequency. Each of the other subsequent centroids is selected based on the dissimilarities of the previously selected centroids. For comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means+

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19

Najadat, Hassan, Rasha Obeidat, and Ismail Hmeidi. "Clustering Generalised Instances Set Approaches for Text Classification." Journal of Information & Knowledge Management 10, no. 01 (2011): 91–107. http://dx.doi.org/10.1142/s0219649211002857.

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This paper introduces three new text classification methods: Clustering-Based Generalised Instances Set (CB-GIS), Multilevel Clustering-Based Generalised Instances Set (MLC_GIS) and Multilevel Clustering-Based, k Nearest Neighbours (MLC-kNN). These new methods aim to unify the strengths and overcome the drawbacks of the three similarity-based text classification methods, namely, kNN, centroid-based and GIS. The new methods utilise a clustering technique called spherical K-means to represent each class by a representative set of generalised instances to be used later in the classification. The

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Tongbram, Simon, Benjamin A. Shimray, and Loitongbam Surajkumar Singh. "Segmentation of image based on k-means and modified subtractive clustering." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1396–403. https://doi.org/10.11591/ijeecs.v22.i3.pp1396-1403.

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Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is p

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21

Tri Martuti, Eviana Tjatur Putri, and Roman Gusmana. "Application of K-Means Clustering for Student Class Division System." Journal of Big Data Analytic and Artificial Intelligence 6, no. 2 (2023): 17–24. https://doi.org/10.71302/jbidai.v6i2.35.

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SMP Negeri 2 Malinau Utara is a junior high school in Desa Putat, Malinau Utara, Malinau, Kalimantan Utara and has 127 students. Currently, the class division process is inefficient and random. On the other hand, the clustering process' class division must be able to provide each class a balanced number of students. This study proposes the grades of Indonesian and English languages, Mathematics, and Natural Sciences for the clustering. K-means is applied to evenly group students based on predetermined value criteria to achieve the expected class formation. K-Means Clustering is an algorithm in

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Negara, Haviz Atma, Achmad Rizaldi Putra, and Ultach Enri. "Clustering Clustering Data Eskspor Buah-Buahan Berdasarkan Negera Tujuan Menggunakan Algoritma K-Means." BINA INSANI ICT JOURNAL 8, no. 1 (2021): 73. http://dx.doi.org/10.51211/biict.v8i1.1506.

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Abstrak: Ekspor merupakan kegiatan ekonomi dalam memasarkan dan jual barang, baik industry, pangan, serta kebutuhan lainnya kepada negara lainnya yang memiliki kurs atau nilai mata uang asing yang lebih besar, tujuannya ialah untuk mencari keuntungan yang sebesar-besarnya. Penelitian ini bertujuan untuk menerapkan data mining dengan metode k-means clustering data ekspor buah-buahan menurut negara tujuannya yang merupakan salah satu komoditas pangan. Penelitian ini menggunakan data pada tahun 2012 sampai 2019 yang diambil melalui situs https://www.bps.go.id. Data diolah dengan mengklasterkan da

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Nanang Lestio Wibowo, Moch Arief Soeleman, and Ahmad Zainul Fanani. "Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (2023): 870–83. http://dx.doi.org/10.29207/resti.v7i4.4997.

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Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, easy to use and fast in clustering data. The K-means method groups data based on k distances and determines the initial centroid randomly as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method for determining the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor c

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Silvi, Rini. "Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia." Jurnal Matematika "MANTIK" 4, no. 1 (2018): 22–31. http://dx.doi.org/10.15642/mantik.2018.4.1.22-31.

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Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed e

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Lei, Jing. "An Analytical Model of College Students’ Mental Health Education Based on the Clustering Algorithm." Mathematical Problems in Engineering 2022 (September 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1880214.

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This paper proposes an improved k-means clustering algorithm to analyze the mental health education of college students. It offers an improved k-means clustering algorithm with optimized centroid selection to address the problems of randomly selected class cluster centroids that lead to inconsistent algorithm results and easily fall into local optimal solutions of the traditional k-means clustering algorithm. The algorithm determines the neighborhood parameter based on the Euclidean distance between the data object and its nearest neighbor in the data set. It counts the object density based on

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Bangun, Monica Natalia, Open Darnius, and Sutarman Sutarman. "OPTIMIZATION MODEL IN CLUSTERING THE HAZARD ZONE AFTER AN EARTHQUAKE DISASTER." SinkrOn 7, no. 3 (2022): 2089–95. http://dx.doi.org/10.33395/sinkron.v7i3.11598.

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There are a large number of approaches to clustering problems, including optimization-based methods involving mathematical programming models to develop efficient and meaningful clustering schemes. Clustering is one of the data labeling techniques. K-means clustering is a partition clustering algorithm that starts by selecting k representative points as the initial centroid. Each point is then assigned to the nearest centroid based on the selected specific proximity measure. This writing is focused on the grouping of post-earthquake hazard zones based on grouping with regard to certain charact

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Ahmad Kasim, Anita, and Siti Uyun Mubarak. "The Implementation of K-means Algorithm for Clustering Traffic Accident Rates on the Highway." Tadulako Science and Technology Journal 1, no. 1 (2020): 7–12. http://dx.doi.org/10.22487/sciencetech.v1i1.15193.

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Introduction : The increase of population in Palu City has result in increased vehicle ownership and increased the risk of traffic problems such as traffic accidents. Sofar, the accident data in the Palu Resort Police Station has not been fully utilized by the interests of related parties. Therefore, the accumulation of data will be processed by data mining techniques. This study aims to cluster the level of accidents in Palu City based on the age of the perpetrators, where the results of the clustering will be used as consideration for the more targeted socialization of traffic accidents. Bas

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Al Ghifari, Muhammad, and Wahyuningdiah Trisari Harsanti Putri. "Clustering Courses Based On Student Grades Using K-Means Algorithm With Elbow Method For Centroid Determination." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 8, no. 1 (2023): 42–46. http://dx.doi.org/10.25139/inform.v8i1.4519.

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Students who have taken courses will receive grades from a performance index with a weight of 0 to 4. The amount of historical student data, particularly on course grades, has the potential to discover new insights. Still, course grades are closed data and are only for academic and management purposes. The research aims to a grouping of courses with high average grades. In this research, the clustering of courses using the k-means clustering algorithm using the elbow method to determine the centroid. Based on the Sum of Squares calculation, the optimal number of clusters with k=2 was obtained.

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Tongbram, Simon, Benjamin A. Shimray, and Loitongbam Surajkumar Singh. "Segmentation of image based on k-means and modified subtractive clustering." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1396. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1396-1403.

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Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is p

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Muhima, Rani Rotul, Muchamad Kurniawan, Septiyawan Rosetya Wardhana, Anton Yudhana, Sunardi Sunardi, and Mitra Adhimukti. "An improved clustering based on K-means for hotspots data." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 1109. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp1109-1117.

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Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster. Experimenta

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Zahra, Sobia, Mustansar Ali Ghazanfar, Asra Khalid, Muhammad Awais Azam, Usman Naeem, and Adam Prugel-Bennett. "Novel centroid selection approaches for KMeans-clustering based recommender systems." Information Sciences 320 (November 2015): 156–89. http://dx.doi.org/10.1016/j.ins.2015.03.062.

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Sathya Narayanan. "Ideal/balance point clustering algorithm." International Journal of Science and Research Archive 10, no. 2 (2023): 618–25. http://dx.doi.org/10.30574/ijsra.2023.10.2.1006.

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Generally clustering is one of the largest used ML techniques to group data points of similar type together. Mostly while grouping data points based upon few popular traditional clustering algorithms like KNN, K-means etc. only the magnitudes of those data points are considered. The deviation of those data points from a particular point said to be the center point or the balance point of the entire dataset is not considered that much, but Here in this Ideal Balance Point clustering algorithm the degree of deviation of all the data points from the balance point is primarily considered. By using

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Wu, Chih-Hung, Chen-Sen Ouyang, Li-Wen Chen, and Li-Wei Lu. "A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering." IEEE Transactions on Fuzzy Systems 23, no. 3 (2015): 701–18. http://dx.doi.org/10.1109/tfuzz.2014.2322495.

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Long, Hoang Minh, and Nicola Delmonte. "K-centroid convergence clustering identification in one-label per type for disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1149–59. https://doi.org/10.11591/ijai.v13.i1.pp1149-1159.

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Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The Kcentroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before a

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Ladha, Girdhar Gopal, and Ravi Kumar Singh Pippal. "An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset." International Journal of Advanced Technology and Engineering Exploration 7, no. 73 (2020): 234–40. http://dx.doi.org/10.19101/ijatee.2020.762109.

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In this paper an efficient distance estimation and centroid selection based on k-means clustering for small and large dataset. Data pre-processing was performed first on the dataset. For the complete study and analysis PIMA Indian diabetes dataset was considered. After pre-processing distance and centroid estimation was performed. It includes initial selection based on randomization and then centroids updations were performed till the iterations or epochs determined. Distance measures used here are Euclidean distance (Ed), Pearson Coefficient distance (PCd), Chebyshev distance (Csd) and Canber

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Kang, Sungmin, Seokjoo Lee, and Jun-ki Min. "An Efficient Clustering Method based on Multi Centroid Set using MapReduce." KIISE Transactions on Computing Practices 21, no. 7 (2015): 494–99. http://dx.doi.org/10.5626/ktcp.2015.21.7.494.

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Fränti, Pasi, and Sami Sieranoja. "Clustering accuracy." Applied Computing and Intelligence 4, no. 1 (2024): 24–44. http://dx.doi.org/10.3934/aci.2024003.

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<abstract> <p>Clustering accuracy (ACC) is one of the most often used measures in literature to evaluate clustering quality. However, the measure is often used without any definition or reference to such a definition. In this paper, we identify the origin of the measure. We give a proper definition for the measure and provide a simple bug fix which allows it to be used also in the case of a mismatch in the number of clusters. We show that the measure belongs to a wider class of set-matching based measures. We compare its properties to centroid index (CI) and normalized mutual infor

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Buatoom, Uraiwan, Waree Kongprawechnon, and Thanaruk Theeramunkong. "Document Clustering Using K-Means with Term Weighting as Similarity-Based Constraints." Symmetry 12, no. 6 (2020): 967. http://dx.doi.org/10.3390/sym12060967.

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In similarity-based constrained clustering, there have been various approaches on how to define the similarity between documents to guide the grouping of similar documents together. This paper presents an approach to use term-distribution statistics extracted from a small number of cue instances with their known classes, for term weightings as indirect distance constraint. As for distribution-based term weighting, three types of term-oriented standard deviations are exploited: distribution of a term in a collection (SD), average distribution of a term in a class (ACSD), and average distributio

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Hoang, Minh Long, and Nicola Delmonte. "K-centroid convergence clustering identification in one-label per type for disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1149. http://dx.doi.org/10.11591/ijai.v13.i1.pp1149-1159.

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<span>Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC<sup>3</sup>I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC<sup>3</sup>I model also includes a

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Clemente, Filipe Manuel, Micael Santos Couceiro, Fernando Manuel Lourenço Martins, and Rui Sousa Mendes. "Using network metrics to investigate football team players' connections: A pilot study." Motriz: Revista de Educação Física 20, no. 3 (2014): 262–71. http://dx.doi.org/10.1590/s1980-65742014000300004.

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The aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on "meso" and "micro" analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity

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Salman, Osama A., and Gábor Hosszú. "A Phenetic Approach to Selected Variants of Arabic and Aramaic Scripts." International Journal of Data Analytics 3, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijda.297519.

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This paper aims to introduce the phenetic method for processing paleographical datasets and evaluating their similarity relationships. The presented numerical taxonomic method was applied for selected varieties of the Arabic and Aramaic scripts. The phenetic model was evaluated by hierarchical clustering and—after applying multidimensional scaling—a centroid-based clustering method. The hierarchical clustering results were presented as dendrograms (phenograms), while the centroid-based results were given in 2- and 3-dimensional Cartesian coordinate systems. The obtained results demonstrate tha

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Syahputra, Ahmad Agung Zefi, Annisa Dwi Atika, Muhammad Adam Aslamsyah, Meida Cahyo Untoro, and Winda Yulita. "Smartphone Price Grouping by Specifications using K-Means Clustering Method." Jurnal Teknik Informatika C.I.T Medicom 13, no. 2 (2021): 64–74. http://dx.doi.org/10.35335/cit.vol13.2021.98.pp59-68.

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The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similari

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Jaya Mabel Rani, A., and A. Pravin. "Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data." Advances in Fuzzy Systems 2022 (March 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/7783196.

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Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the c

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Romanuke, Vadim, Svitlana Merinova, and Hanna Yehoshyna. "Optimized Centroid-Based Clustering of Dense Nearly-square Point Clouds by the Hexagonal Pattern." Electrical, Control and Communication Engineering 19, no. 1 (2023): 29–39. http://dx.doi.org/10.2478/ecce-2023-0005.

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Abstract An approach to optimize centroid-based clustering of flat objects is suggested, which is practically important for efficiently solving metric facility location problems. In such problems, the task is to find the best warehouse locations to optimally service a given set of consumers. An example is assigning mobiles to base stations of a wireless communication network. We suggest a hexagonal-pattern-based approach to partition flat nodes into clusters quicker than the k-means algorithm and its modifications do. First, a hexagonal cell lattice is applied to nodes to approximately determi

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Li, Yi. "Weighted Centroid Localization Algorithm Based on MEA-BP Neural Network and DBSCAN Clustering." Journal of Physics: Conference Series 2363, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1742-6596/2363/1/012006.

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In order to overcome RSSI ranging error and improve the accuracy of positioning results, a weighted localization algorithm based on MEA-BP Neural Network and DBSCAN clustering is proposed in this paper. This algorithm uses MEA-BP Neural Network (MEA-BP NN) model to optimize ranging information firstly, then it uses trilateral measurement method to get multiple initial localization results about unknown node and form a set. After clustering the results by DBSCAN and eliminating noise points, the estimated coordinate of unknown node in each cluster is obtained by using the weighted centroid loca

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Andrean Samuel Siahaan, Rusmin Saragih, and Magdalena Simanjuntak. "Penerapan Metode K-Means Clustering untuk Pengelompokan Minat Konsumen terhadap Pengguna Jasa Layanan pada Kantor Pos Binjai." Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam 2, no. 5 (2024): 92–102. http://dx.doi.org/10.62383/polygon.v2i5.236.

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This research aims to apply the K-Means Clustering method in grouping consumer interests regarding the use of services at the Binjai Post Office. The Post Office is part of a state-owned enterprise in North Sumatra Province with the main task of providing postal and logistics services. Postal services remain one of the most important means of communication, especially for sending packages, letters, and documents. However, with various services and diverse consumer needs, post offices can provide more effective and relevant services. The K-Means Clustering method is a classification technique b

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Musdar, Izmy Alwiah, and Azhari SN. "Metode RCE-Kmeans untuk Clustering Data." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 157. http://dx.doi.org/10.22146/ijccs.7544.

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AbstrakTelah banyak metode yang dikembangkan untuk memecahkan berbagai masalah clustering. Salah satunya menggunakan metode-metode dari bidang kecerdasan kelompok seperti Particle Swarm Optimization (PSO). Metode Rapid Centroid Estimation (RCE) merupakan salah satu metode clustering yang berbasis PSO. RCE, seperti varian PSO clustering lainnya, memiliki kelebihan yaitu hasil clustering tidak tergantung pada inisialisasi pusat cluster awal. RCE juga memiliki waktu komputasi yang jauh lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Swarm Clustering (PSC) dan modified Particle Sw

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Rani, Rotul Muhima, Kurniawan Muchamad, Rosetya Wardhana Septiyawan, Yudhana Anton, Sunardi, and Adhimukti Mitra. "An improved clustering based on K-means for hotspots data." An improved clustering based on K-means for hotspots data 31, no. 2 (2023): 1109–17. https://doi.org/10.11591/ijeecs.v31.i2.pp1109-1117.

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Riau province is one of the provinces in Indonesia where forest fires frequently occur every year. Hotspot data is geothermal points and they can be utilized as an indicator of forest fires. Clustering’s method can be used to analyze potential forest fires from hotspot data’s cluster pattern. In this study, hybrid genetic algorithm polygamy with K-means (GAP K-means) was used for hotspot data clustering. GA polygamy was used to determine the initial centroid of K-means. It was used to solve the sensitivity of K-means to the initial centroid, and to find the optimal solution faster.

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Prades, José, Gonzalo Safont, Addisson Salazar, and Luis Vergara. "Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering." Remote Sensing 12, no. 21 (2020): 3585. http://dx.doi.org/10.3390/rs12213585.

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Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densitie

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Aparajita, Akankshya, Shrabanee Swagatika, and Debabrata Singh. "Comparative Analysis of Clustering Techniques in Cloud For Effective Load Balancing." International Journal of Engineering & Technology 7, no. 3.4 (2018): 47. http://dx.doi.org/10.14419/ijet.v7i3.4.14674.

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Clustering is used as an important procedure in the process of data mining, where information of large datasets are transformed into meaningful and concise data. It performs activities like pattern representation, using of clustering algorithms and their validation, data abstraction and finally result generated. Clustering has many categories of algorithms such as partition-based, hierarchical-based, density-based, grid-based etc. Partition-based is the centroid-based clustering. Hierarchical-based clustering is link-based. Density-based is clustering is focused on area of higher density in th

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