Relevant bibliographies by topics / Centroid-based clustering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Centroid-based clustering'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Centroid-based clustering"

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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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Dissertations / Theses on the topic "Centroid-based clustering"

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Cañas, Daniel Alberto. "Generalizations and unification of centroid-based clustering methods." NCSU, 2004. http://www.lib.ncsu.edu/theses/available/etd-11052004-022839/.

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There are many clustering methods that are referred to as k-means-like. We give the minimal necessary and sufficient components for the mechanism of the k-means (iterative and partitional) clustering method of a finite set of objects, X. Thus k-means is generalized and the methods that mimic k-means are unified. We name these k-center clustering methods. The fundamental mechanism of k-center methods exposes the usual misconceptions of k-means such as (a) ``distance" satisfies some of properties of a mathematical metric, (b) there is a need to measure ``distance" between objects in X, and (c) t
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Pettersson, Christoffer. "Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. They contain no information regarding prior labeling or categorization which creates a need for an unsupervised learning approach using solely the raw text based content as data. The project investigates state-of-the-art concepts like bag-of-words for calculating term
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Cañas, Daniel A. "Generalizations and unification of centroid-based clustering methods." 2004. http://www.lib.ncsu.edu/theses/available/etd-11052004-022839/unrestricted/etd.pdf.

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Book chapters on the topic "Centroid-based clustering"

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Sarang, Poornachandra. "Centroid-Based Clustering." In Thinking Data Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-02363-7_9.

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Kubek, Mario. "Centroid-Based Library Management and Document Clustering." In Studies in Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23136-1_7.

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Schütz, Lars, Korinna Bade, and Andreas Nürnberger. "Evaluating Prototypes and Criticisms for Explaining Clustered Contributions in Digital Public Participation Processes." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39059-3_29.

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AbstractWe examine the use of prototypes and criticisms for explaining clusterings in digital public participation processes of the e-participation domain. These processes enable people to participate in various life areas such as landscape planning by submitting contributions that express their opinions or ideas. Clustering groups similar contributions together. This supports citizens and public administrations, the main participants in digital public participation processes, in exploring the submitted contributions. However, explaining clusterings remains a challenge. For this purpose, we co
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Xie, Yifan, Xing Wang, Long Zhang, and Guoxian Yu. "Affinity Propagation Clustering Using Centroid-Deviation-Distance Based Similarity." In Human Brain and Artificial Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1398-5_21.

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Ray, Soujanya, and Anupam Ghosh. "Centroid-Based Hierarchy Preserving Clustering Algorithm Using Lighthouse Scanning." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2188-1_22.

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Ismail, Fatma Helmy, Ahmed Fouad Ali, Saleh Esmat, and Aboul Ella Hassanien. "Newcastle Disease Virus Clustering Based on Swarm Rapid Centroid Estimation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27400-3_32.

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Xue, Shanliang, Mengying Li, and Peiru Yang. "Centroid Location Technology Based on Fuzzy Clustering and Data Consistency." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_13.

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Thulasidas, Manoj. "A Quality Metric for K-Means Clustering Based on Centroid Locations." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22137-8_16.

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Mishra, Shashi Kant, D. Srinivasu, Barre Praneeth Reddy, and Rohit Jain. "Uncovering insights in cancer research with centroid-based clustering on big data." In Artificial Intelligence Revolutionizing Cancer Care. CRC Press, 2024. https://doi.org/10.1201/9781003571339-5.

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Praveen, V., V. Hemalatha, and P. Gomathi. "A Nearest Centroid Classifier-Based Clustering Algorithm for Solving Vehicle Routing Problem." In Lecture Notes in Networks and Systems. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3812-9_59.

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Conference papers on the topic "Centroid-based clustering"

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Hennessey, Samuel L., Francis J. Williams, and Ludmila I. Kuncheva. "Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705263.

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Nuraeni, Fitri, Abdul Syukur, Aris Marjuni, Nova Rijati, and Dede Kurniadi. "Comparison of Centroid-Based Clustering Model Performance on Categorical Dataset." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10956869.

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Mufti, Jalaludin Shofa, and Arian Dhini. "Comparative Analysis of Centroid-Based and Density-Based Clustering for Indonesian Earthquake Data." In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2024. http://dx.doi.org/10.1109/ic3ina64086.2024.10732579.

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Thaiprayoon, Santipong, Herwig Unger, and Mario Kubek. "Graph and Centroid-based Word Clustering." In NLPIR 2020: 4th International Conference on Natural Language Processing and Information Retrieval. ACM, 2020. http://dx.doi.org/10.1145/3443279.3443290.

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Anindya, Imrul Chowdhury, and Murat Kantarcioglu. "Adversarial Anomaly Detection Using Centroid-Based Clustering." In 2018 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2018. http://dx.doi.org/10.1109/iri.2018.00009.

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Atchaya, V., and R. Vanitha. "An Optimal centroid based actionable 3D subspace clustering." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033862.

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Abdalgader, Khaled. "Centroid-Based Clustering Using Sentential Embedding Similarity Measure." In 2022 International Conference on Electrical and Computing Technologies and Applications (ICECTA). IEEE, 2022. http://dx.doi.org/10.1109/icecta57148.2022.9990526.

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Hossain, A. S. M. Shahadat. "Customer segmentation using centroid based and density based clustering algorithms." In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT). IEEE, 2017. http://dx.doi.org/10.1109/eict.2017.8275249.

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Li, Ximing, Jihong Ouyang, Xiaotang Zhou, and Bo Fu. "Adaptive Centroid-Based Clustering Algorithm for Text Document Data." In 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2014. http://dx.doi.org/10.1109/paap.2014.13.

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"ROBUST CENTROID-BASED CLUSTERING USING DERIVATIVES OF PEARSON CORRELATION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001062601970203.

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