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Journal articles on the topic 'Adaptive K-means'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Zhang, Yiwen, Yuanyuan Zhou, Xing Guo, et al. "Self-Adaptive K-Means Based on a Covering Algorithm." Complexity 2018 (August 1, 2018): 1–16. http://dx.doi.org/10.1155/2018/7698274.

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The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters b

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12

Chen, Min, and Fu Yan Wang. "Context Quantization Based on the Modified K-Means Clustering." Advanced Materials Research 756-759 (September 2013): 4068–72. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4068.

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The context quantization forsource based on the modified K-means clustering algorithm is present in this paper. In this algorithm, the adaptive complementary relative entropy between two conditional probability distributions, which is used as the distance measure for K-means instead, is formulated to describe the similarity of these two probability distributions. The rules of the initialized centers chosen for K-means are also discussed. The proposed algorithm will traverse all possible number of the classes to search the optimal one which is corresponding to the shortest adaptive code length.

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13

Deng, Ai Ping, Ben Xiao, and Hui Yong Yuan. "Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value." Advanced Materials Research 532-533 (June 2012): 1373–77. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1373.

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In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm ca

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14

JianJiao Chen, Anping Song, and Wu Zhang. "Hybrid Clustering Methods Based on Adaptive K-harmonic Means." International Journal of Advancements in Computing Technology 4, no. 6 (2012): 10–23. http://dx.doi.org/10.4156/ijact.vol4.issue6.2.

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15

Wu, Zhijun, Rong Li, and Changliang Li. "Adaptive Speech Information Hiding Method Based on K-Means." IEEE Access 8 (2020): 23308–16. http://dx.doi.org/10.1109/access.2020.2970194.

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16

Sulaiman, Siti, and Nor Mat Isa. "Adaptive fuzzy-K-means clustering algorithm for image segmentation." IEEE Transactions on Consumer Electronics 56, no. 4 (2010): 2661–68. http://dx.doi.org/10.1109/tce.2010.5681154.

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17

Imran, Shaik Mohammed, Muthukumaran M, and V.Tharakeswari. "Clustering of massive datasets using an Adaptive and efficient K-Means approach." International Journal of Scientific Methods in Engineering and Management 01, no. 02 (2023): 33–40. http://dx.doi.org/10.58599/ijsmem.2023.1204.

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In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work

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18

Rajab, Maha A., and Loay E. George. "Stamps extraction using local adaptive k-means and ISODATA algorithms." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 137–45. https://doi.org/10.11591/ijeecs.v21.i1.pp137-145.

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One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cl

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19

Bhardwaj, Amit, and Parneet Kaur. "Adaptive Distributed Intrusion Detection using Hybrid K-means SVM Algorithm." International Journal of Computer Applications 74, no. 15 (2013): 33–37. http://dx.doi.org/10.5120/12963-0145.

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20

Patel, Bhagwati Charan, and G. R. Sinha. "An Adaptive K-means Clustering Algorithm for Breast Image Segmentation." International Journal of Computer Applications 10, no. 4 (2010): 35–38. http://dx.doi.org/10.5120/1467-1982.

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21

Wang, Xiao-Dong, Rung-Ching Chen, Fei Yan, Zhi-Qiang Zeng, and Chao-Qun Hong. "Fast Adaptive K-Means Subspace Clustering for High-Dimensional Data." IEEE Access 7 (2019): 42639–51. http://dx.doi.org/10.1109/access.2019.2907043.

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22

Mat Isa, Nor, Samy Salamah, and Umi Ngah. "Adaptive fuzzy moving K-means clustering algorithm for image segmentation." IEEE Transactions on Consumer Electronics 55, no. 4 (2009): 2145–53. http://dx.doi.org/10.1109/tce.2009.5373781.

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23

A. Rajab, Maha, and Loay E. George. "Stamps extraction using local adaptive k- means and ISODATA algorithms." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 137. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp137-145.

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<span>One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate b

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24

Zhang, Rui, Xuelong Li, Hongyuan Zhang, and Feiping Nie. "Deep Fuzzy K-Means With Adaptive Loss and Entropy Regularization." IEEE Transactions on Fuzzy Systems 28, no. 11 (2020): 2814–24. http://dx.doi.org/10.1109/tfuzz.2019.2945232.

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25

Alanazi, Nafea, Finlay Smith, and Attracta Brennan. "Adaptive Learning System (ALS) using Fuzzy Logic and K-means." International Journal for Digital Society 13, no. 1 (2022): 1781–86. http://dx.doi.org/10.20533/ijds.2040.2570.2022.0222.

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26

Moftah, Hossam M., Ahmad Taher Azar, Eiman Tamah Al-Shammari, Neveen I. Ghali, Aboul Ella Hassanien, and Mahmoud Shoman. "Adaptive k-means clustering algorithm for MR breast image segmentation." Neural Computing and Applications 24, no. 7-8 (2013): 1917–28. http://dx.doi.org/10.1007/s00521-013-1437-4.

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27

Guo, Guang Nan, Yong Gang Yun, Mei Chu, Hong Yan Shi, and Ke Gong Yin. "Application of Density-Based Adaptive K-Means Clustering Algorithm in Web Log Mining." Advanced Materials Research 433-440 (January 2012): 5152–56. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5152.

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Aiming at main challenges of Web mining and personalized service currently, basic K-Means algorithm of clustering techniques was researched, including algorithm flow and limitations. To solve shortcomings of pre-determining cluster number, heavily dependent on initial center selection and particularly sensitive to noise as well as edge data in basic K-Means algorithm, improved density-based adaptive K-Means algorithm was presented. It conducts steps of initial classification and K means iterative to reduce impact of above problems and improve clustering quality. Experiments on Web log clusteri

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28

Mr., Mohan Raj C. S., and Srikanth V. Dr. "K-Means and Fuzzy C-Means Algorithm for Mammogramy Image Segmentation." Sangrathan Journal, UGC Care Listed Journal 4, no. 1 (2024): 203–15. https://doi.org/10.5281/zenodo.11000974.

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One of the foremost challenges in image analysis is image segmentation. The majority of medical applications often involve trained operators extracting images from targeted regions that may be physically distinct but statistically indistinguishable. Also, Image segmentation is time-consuming and has poor reproducibility often subjected to manual errors and biases. Identification of clusters in given data is another challenge during clustering. K-means is a widely used clustering technique that divides the data into K different clusters. In this strategy, clusters are specified in advance, whic

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29

Wang Jianqiang, 王建强, 樊彦国 Fan Yanguo, 李国胜 Li Guosheng, and 禹定峰 Yu Dingfeng. "Adaptive Point Cloud Reduction Based on Multi Parameter k-Means Clustering." Laser & Optoelectronics Progress 58, no. 6 (2021): 0610008. http://dx.doi.org/10.3788/lop202158.0610008.

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30

Jin, Qibing, Nan Lin, and Yuming Zhang. "K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony." Algorithms 14, no. 2 (2021): 53. http://dx.doi.org/10.3390/a14020053.

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K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy,

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31

Liao, Jiahao, and Shan Liu. "An Improved Segmented MTI Filter Based on a Clustering Algorithm." Journal of Physics: Conference Series 2447, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1742-6596/2447/1/012008.

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Abstract A K-Means clustering algorithm modified MTI (Moving Target Indication) filter is proposed for refining signal processing of the array environment in response to the current demand for clutter suppression by array adaptive technology. The method first uses the clutter distribution type and distance as parameters of the K-Means clustering algorithm to divide the echo’s into different clutter distance segments. Then an adaptive MTI filter is used to filter each clutter distance segment separately. The identification was carried out using measured ground radar echo data. The results show

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32

Pamungkas, Danar Putra, and Firmansyah Mukti Wijaya. "Analisis Hasil Segmentasi Citra Daun Bawang Dengan Metode Adaptive Thesholding dan K-Means Clustering." JOINTECS (Journal of Information Technology and Computer Science) 8, no. 3 (2023): 95. http://dx.doi.org/10.31328/jointecs.v8i3.4791.

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Segmentasi citra yang akurat memiliki dampak signifikan pada hasil analisis citra secara keseluruhan. Penelitian ini bertujuan untuk membandingkan metode Adaptive Thresholding dan K-Means Clustering dalam segmentasi citra daun bawang merah dengan latar belakang yang berbeda. Dengan menggunakan analisis kuantitatif terhadap 25 citra daun bawang yang beragam, hasil penelitian menunjukkan bahwa Adaptive Thresholding menghasilkan segmentasi yang memuaskan dalam skala warna hitam dan putih, sementara K-Means Clustering dengan ekstraksi fitur juga memberikan hasil yang memuaskan. Analisis berbasis a

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33

Liu, Yuanyuan. "Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid." Mathematical Problems in Engineering 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/8478790.

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In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according

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34

M., I., S. S., and U. V. "MRI Brain Image segmentation using Adaptive Thresholding and K-means Algorithm." International Journal of Computer Applications 167, no. 8 (2017): 11–15. http://dx.doi.org/10.5120/ijca2017914330.

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35

JiMing, Ma, Li XiaoJiao, Su RiJian, and Zhang Xiang Mei. "An Adaptive Initial Cluster Center Selection K-means Algorithm and Implementation." Information Technology Journal 12, no. 20 (2013): 5665–68. http://dx.doi.org/10.3923/itj.2013.5665.5668.

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36

Maliatski, B., and O. Yadid-Pecht. "Hardware-driven adaptive k-means clustering for real-time video imaging." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 1 (2005): 164–66. http://dx.doi.org/10.1109/tcsvt.2004.839977.

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37

Wu, Ziheng, and Zixiang Wu. "An Enhanced Regularized k-Means Type Clustering Algorithm With Adaptive Weights." IEEE Access 8 (2020): 31171–79. http://dx.doi.org/10.1109/access.2020.2972333.

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38

Ganesh, M., M. Naresh, and C. Arvind. "MRI Brain Image Segmentation Using Enhanced Adaptive Fuzzy K-Means Algorithm." Intelligent Automation & Soft Computing 23, no. 2 (2017): 325–30. http://dx.doi.org/10.1080/10798587.2016.1231472.

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39

Chinrungrueng, C., and C. H. Sequin. "Optimal adaptive k-means algorithm with dynamic adjustment of learning rate." IEEE Transactions on Neural Networks 6, no. 1 (1995): 157–69. http://dx.doi.org/10.1109/72.363440.

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40

Huang, Xiaodi, Minglun Ren, and Xiaoxi Zhu. "A Novel Improved K-Means Algorithm Based on Parameter Adaptive Selection." Journal of Physics: Conference Series 1549 (June 2020): 042005. http://dx.doi.org/10.1088/1742-6596/1549/4/042005.

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41

Chen, Tse-Wei, and Shao-Yi Chien. "Bandwidth Adaptive Hardware Architecture of K-Means Clustering for Video Analysis." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 6 (2010): 957–66. http://dx.doi.org/10.1109/tvlsi.2009.2017543.

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42

Huang, Rongjie, Chuanxu Wang, Hao Li, and Guoyong Ye. "A point cloud denoising method combining K-means + + and adaptive threshold." Journal of Physics: Conference Series 3032, no. 1 (2025): 012035. https://doi.org/10.1088/1742-6596/3032/1/012035.

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Abstract To address the issue of large-scale noise clusters and small-scale scattered noise in the point cloud of aircraft wing-fuselage docking surfaces, this paper proposes a multi-scale hybrid filtering denoising method. By utilizing the k-means++ clustering algorithm and introducing a density weight factor along with a dynamic neighborhood radius mechanism, large-scale noise clusters are effectively segmented and removed. Additionally, adaptive radius filtering is employed to eliminate residual fine noise, achieving a hierarchical denoising process across different scales. Validation using

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43

Wang, Zhibin, Kaiyi Wang, Shouhui Pan, and Yanyun Han. "Segmentation of Crop Disease Images with an Improved K-means Clustering Algorithm." Applied Engineering in Agriculture 34, no. 2 (2018): 277–89. http://dx.doi.org/10.13031/aea.12205.

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Abstract. Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distanc

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44

Ikotun, Abiodun M., and Absalom E. Ezugwu. "Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets." Applied Sciences 12, no. 23 (2022): 12275. http://dx.doi.org/10.3390/app122312275.

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Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to th

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Wang, Xue Mei, Yi Zhuo Guo, and Gui Jun Liu. "Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means." Advanced Materials Research 760-762 (September 2013): 2194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2194.

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Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy

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46

Yang Xiongwei, 杨雄伟, 赵峰 Zhao Feng, 赵林仙 Zhao Linxian та 孟昭 Meng Zhao. "基于K-means的自适应概率整形信号相位恢复算法". Acta Optica Sinica 42, № 9 (2022): 0906001. http://dx.doi.org/10.3788/aos202242.0906001.

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47

Singh, Prashuma, and Angad Singh. "Energy Efficiency through K-means++ with Adaptive Leach in Wireless Sensor Network." International Journal of Computer Applications 182, no. 16 (2018): 14–18. http://dx.doi.org/10.5120/ijca2018917821.

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48

Du, Li, Yuan Du, and Mau-Chung Frank Chang. "A Reconfigurable 64-Dimension K-Means Clustering Accelerator With Adaptive Overflow Control." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 4 (2020): 760–64. http://dx.doi.org/10.1109/tcsii.2019.2922657.

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49

Tai, Haowei, Mawia Khairalseed, and Kenneth Hoyt. "Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering." Ultrasonics 102 (March 2020): 105987. http://dx.doi.org/10.1016/j.ultras.2019.105987.

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Ding, Shifei, Xiao Xu, Shuyan Fan, and Yu Xue. "Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors." Soft Computing 22, no. 14 (2017): 4573–83. http://dx.doi.org/10.1007/s00500-017-2640-5.

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