Relevant bibliographies by topics / Adaptive K-means

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Academic literature on the topic 'Adaptive K-means'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Adaptive K-means"

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Hedar, Abdel-Rahman, Abdel-Monem Ibrahim, Alaa Abdel-Hakim, and Adel Sewisy. "K-Means Cloning: Adaptive Spherical K-Means Clustering." Algorithms 11, no. 10 (2018): 151. http://dx.doi.org/10.3390/a11100151.

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We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The propo
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Kanjanawattana, Sarunya. "A Novel Outlier Detection Applied to an Adaptive K-Means." International Journal of Machine Learning and Computing 9, no. 5 (2019): 569–74. http://dx.doi.org/10.18178/ijmlc.2019.9.5.841.

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Madhusmita, Sahu, Parvathi K., and Vamsi Krishna M. "Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 810–17. https://doi.org/10.11591/ijece.v7i2.pp810-817.

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Image segmentation takes a major role for analyzing the area of interest in image processing. Many researchers have used different types of techniques for analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as com
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Sahu, Madhusmita, K. Parvathi, and M. Vamsi Krishna. "Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 810. http://dx.doi.org/10.11591/ijece.v7i2.pp810-817.

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<p>Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result
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Aradnia, Amir, Maryam Amir Haeri, and Mohammad Mehdi Ebadzadeh. "Adaptive Explicit Kernel Minkowski Weighted K-means." Information Sciences 584 (January 2022): 503–18. http://dx.doi.org/10.1016/j.ins.2021.10.048.

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YONG Zhou, and Haibin SHI. "Adaptive K-means clustering for Color Image Segmentation." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 10 (2011): 216–23. http://dx.doi.org/10.4156/aiss.vol3.issue10.27.

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Kaur, Manjinder, Navjot Kaur, and Harkamaldeep Singh. "Adaptive K-Means Clustering Techniques For Data Clustering." International Journal of Innovative Research in Science, Engineering and Technology 03, no. 09 (2014): 15851–56. http://dx.doi.org/10.15680/ijirset.2014.0309009.

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Song, Chengyun, Zhining Liu, Yaojun Wang, Feng Xu, Xingming Li, and Guangmin Hu. "Adaptive phase k-means algorithm for waveform classification." Exploration Geophysics 49, no. 2 (2018): 213–19. http://dx.doi.org/10.1071/eg16111.

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BELLO-ORGAZ, GEMA, HÉCTOR D. MENÉNDEZ, and DAVID CAMACHO. "ADAPTIVE K-MEANS ALGORITHM FOR OVERLAPPED GRAPH CLUSTERING." International Journal of Neural Systems 22, no. 05 (2012): 1250018. http://dx.doi.org/10.1142/s0129065712500189.

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The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a
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Debelee, Taye Girma, Friedhelm Schwenker, Samuel Rahimeto, and Dereje Yohannes. "Evaluation of modified adaptive k-means segmentation algorithm." Computational Visual Media 5, no. 4 (2019): 347–61. http://dx.doi.org/10.1007/s41095-019-0151-2.

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Dissertations / Theses on the topic "Adaptive K-means"

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Medlej, Maguy. "Big data management for periodic wireless sensor networks." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2029/document.

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Les recherches présentées dans ce mémoire s’inscrivent dans le cadre des réseaux decapteurs périodiques. Elles portent sur l’étude et la mise en oeuvre d’algorithmes et de protocolesdistribués dédiés à la gestion de données volumineuses, en particulier : la collecte, l’agrégation etla fouille de données. L’approche de la collecte de données permet à chaque noeud d’adapter sontaux d’échantillonnage à l’évolution dynamique de l’environnement. Par ce modèle le suréchantillonnageest réduit et par conséquent la quantité d’énergie consommée. Elle est basée surl’étude de la dépendance de la variance
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Geigel, Arturo. "Unsupervised Learning Trojan." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/17.

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This work presents a proof of concept of an Unsupervised Learning Trojan. The Unsupervised Learning Trojan presents new challenges over previous work on the Neural network Trojan, since the attacker does not control most of the environment. The current work will presented an analysis of how the attack can be successful by proposing new assumptions under which the attack can become a viable one. A general analysis of how the compromise can be theoretically supported is presented, providing enough background for practical implementation development. The analysis was carried out using 3 selected
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WANG, BO-SHINE, and 王柏勝. "Using k-means clustering algorithm-based robust adaptive clustering analysis method for software fault prediction." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/64607850323904520947.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>102<br>Software fault is an error situation of the software system because of wrong specification and inappropriate development of configuration. Almost all kinds of work related to the software system until now. So, the problem of reliability of software system has become one of the key elements between software develop process and software engineering task. At present, most of the studies focused on supervised learning, but some people think that semi-supervised learning and unsupervised learning are necessary. In this paper, we propose k-means clustering algorithm
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Wu, Sheng-Kong, and 吳盛宏. "Combining Adaptive Resonance Theory and K-Means Method for Data Clustering - On-line Game as an Example." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/20381069759338972309.

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碩士<br>玄奘大學<br>資訊科學學系碩士班<br>96<br>The application in company is more and more extensive at Data Mining and Neural Network. The company can use these method to digging new customer and preserving old customer. In Data mining and Adaptive Resonance Theory, data clustering is the most used. This article mainly inquired into the difference of data clustering, advantage ,and disadvantage between K-means of data mining and ART of neural network. And we combined and compared similar each other when we assumed the clustering value number fix for 5% with two method. This research also used the data of a
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Liao, Chien_lun, and 廖建倫. "Integration of Adaptive Resonance Theory II Neural Network and Genetic K-means Algorithms for Recommendation Agent in Data Mining." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/34476671044854116843.

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碩士<br>國立臺北科技大學<br>生產系統工程與管理研究所<br>90<br>The neural networks and genetic algorithms are also feasible for clustering analysis in data mining. Artificial neural networks (ANNs) and genetic algorithms (GAs) have been applied in many areas and obtained very promising results. Kuo and his colleagues have presented that using self-organization feature maps (SOM) network to determine the number of clusters and the starting points and then employing the K-means method to find the final solution, can provide very good solution. Besides, Kuo and his colleagues also proved that K-means can be replaced
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CHEN, CHIEN YU, and 陳建佑. "Improving k-means Algorithm for Finding Readers’ Adaption Book Recommendations." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/42045299028720592906.

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碩士<br>南台科技大學<br>資訊管理系<br>97<br>In the past, libraries’ records only record whether books have been returned or not. These records will not be stored after borrowers have returned the books. However, due to the advances of technology in recent years, borrower’s records can now be easily recorded and stored at the same time. These records become larger as time progresses. Nowadays, in order to improve its utility rate, libraries have evolved to actively recommending which books to read. This is done by associating the interests of readers to types of books available. The purpose of this study is
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Books on the topic "Adaptive K-means"

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Schreiber, Thomas. A voronoi diagram based adaptive k-means-type clustering algorithm for multidimensional weighted data. Universität Kaiserslautern, 1991.

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Book chapters on the topic "Adaptive K-means"

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Olszewski, Dominik. "Asymmetric k-Means Algorithm." In Adaptive and Natural Computing Algorithms. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20267-4_1.

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Aggarwal, Ankit, Amit Deshpande, and Ravi Kannan. "Adaptive Sampling for k-Means Clustering." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03685-9_2.

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Mace, Alex, Roberto Sommariva, Zoë Fleming, and Wenjia Wang. "Adaptive K-Means for Clustering Air Mass Trajectories." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23878-9_1.

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Płoński, Piotr, and Krzysztof Zaremba. "Full and Semi-supervised k-Means Clustering Optimised by Class Membership Hesitation." In Adaptive and Natural Computing Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37213-1_23.

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Mucha, Hans-Joachim, and Hans-Georg Bartel. "An Accelerated K-Means Algorithm Based on Adaptive Distances." In Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24466-7_5.

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Peng, Yihang, Qinghua Zhang, Zhihua Ai, and Xuechao Zhi. "Adaptive K-means Algorithm Based on Three-Way Decision." In Rough Sets. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21244-4_29.

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Lan, Yinhe, Zhenyu Weng, and Yuesheng Zhu. "Center-Adaptive Weighted Binary K-means for Image Clustering." In Advances in Multimedia Information Processing – PCM 2017. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_40.

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Vemuri, B. C., S. Rahman, and J. Li. "Multiresolution adaptive K-means algorithm for segmentation of brain MRI." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60697-1_121.

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Sun, Jiankai, Zhong Li, Fengyuan Zou, and Yunchu Yang. "Adaptive Determining for Optimal Cluster Number of K-Means Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_59.

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Nieddu, Luciano, Giuseppe Manfredi, and Salvatore D’Acunto. "Automatic 3D Image Segmentation Using Adaptive k-means on Brain MRI." In Informatics Engineering and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25453-6_16.

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Conference papers on the topic "Adaptive K-means"

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Puri, Digvijay, and Dr Deepak Gupta. "Enhancing K-Means Clustering with Data-Driven Initialization and Adaptive Distance Measures." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00099.

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Zhou, Jingyang, Shuanglong Deng, Benxiang Tan, et al. "Adaptive K-Means Based Sampling Frequency Offset Compensation for Ultra-Wideband System." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653195.

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TS, Kiran Babu, Arun Francis G, Sathya R, A. Vijayalakshmi, Kottaimalai Ramaraj, and Thilagaraj M. "Hybrid Artificial Bee Colony and Adaptive Fuzzy K-Means for Detecting Abnormalities in Brain MR Images." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10864195.

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Shenghui, Wang, and Li Hanbing. "Adaptive K-valued K-means clustering algorithm." In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, 2020. http://dx.doi.org/10.1109/icmcce51767.2020.00316.

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Chen, Hailin, Xiuqing Wu, and Junhua Hu. "Adaptive K-means clustering algorithm." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.750002.

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Xu, Qinghui, Daniel Migault, stephane senecal, and Stanislas Francfort. "K-means and adaptive k-means algorithms for clustering DNS traffic." In 5th International ICST Conference on Performance Evaluation Methodologies and Tools. ACM, 2011. http://dx.doi.org/10.4108/icst.valuetools.2011.245598.

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Zhou, Tao, Yan-Ning Zhang, He-Jing Yuan, and Hui-Ling Lu. "Rough k-means Cluster with Adaptive Parameters." In Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370674.

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Zhou, An, Famao Mei, Zhenwei Gu, Hao Huang, and Siyuan Dong. "AGK: the Adaptive Grid K-means algorithm." In 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), edited by Tao Zhang. SPIE, 2022. http://dx.doi.org/10.1117/12.2641215.

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Chai, Rong, Xianlei Ge, and Qianbin Chen. "Adaptive K-Harmonic Means clustering algorithm for VANETs." In 2014 14th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2014. http://dx.doi.org/10.1109/iscit.2014.7011907.

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Darken, C., and J. Moody. "Fast adaptive k-means clustering: some empirical results." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137720.

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Reports on the topic "Adaptive K-means"

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Brassil, Anthony, Christopher G. Gibbs, and Callum Ryan. Boundedly Rational Expectations and the Optimality of Flexible Average Inflation Targeting. Reserve Bank of Australia, 2025. https://doi.org/10.47688/rdp2025-02.

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Expectations play a central role in the transmission of monetary policy, but how people form expectations is widely debated. We revisit optimal monetary policy design in a model with behavioural expectations that nest rational and adaptive learning beliefs as special cases, and approximates several alternative expectations theories, such as myopic or level-&lt;i&gt;k&lt;/i&gt; expectations. Optimal policy is a weighted average inflation target plus adjustments for belief persistence and constraints faced by the central bank (information frictions and the zero lower bound). Optimal policy is we
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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