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Journal articles on the topic 'K-means clustering'

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Published: 4 June 2021

Last updated: 25 May 2024

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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 (October 6, 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 proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm.

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2

Jhun, Myoungshic. "BOOTSTRAPPING K-MEANS CLUSTERING." Journal of the Japanese Society of Computational Statistics 3, no. 1 (1990): 1–14. http://dx.doi.org/10.5183/jjscs1988.3.1.

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3

Timmerman, Marieke E., Eva Ceulemans, Kim De Roover, and Karla Van Leeuwen. "Subspace K-means clustering." Behavior Research Methods 45, no. 4 (March 23, 2013): 1011–23. http://dx.doi.org/10.3758/s13428-013-0329-y.

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4

Xiao, Ethan. "Comprehensive K-Means Clustering." Journal of Computer and Communications 12, no. 03 (2024): 146–59. http://dx.doi.org/10.4236/jcc.2024.123009.

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5

Yu, Hengjun, Kohei Inoue, Kenji Hara, and Kiichi Urahama. "A Robust K-Means for Document Clustering." Journal of the Institute of Industrial Applications Engineers 6, no. 2 (April 25, 2018): 60–65. http://dx.doi.org/10.12792/jiiae.6.60.

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6

Madhuri, K., and Mr K. Srinivasa Rao. "Social Media Analysis using Optimized K-Means Clustering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 953–57. http://dx.doi.org/10.31142/ijtsrd21558.

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Ravindran, R. Malathi, and Dr Antony Selvadoss Thanamani. "K-Means Document Clustering using Vector Space Model." Bonfring International Journal of Data Mining 5, no. 2 (July 31, 2015): 10–14. http://dx.doi.org/10.9756/bijdm.8076.

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8

HUA, C., Q. CHEN, H. WU, and T. WADA. "RK-Means Clustering: K-Means with Reliability." IEICE Transactions on Information and Systems E91-D, no. 1 (January 1, 2008): 96–104. http://dx.doi.org/10.1093/ietisy/e91-d.1.96.

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9

Jain, Preeti, and Bala Buksh. "Accelerated K-means Clustering Algorithm." International Journal of Information Technology and Computer Science 8, no. 10 (October 8, 2016): 39–46. http://dx.doi.org/10.5815/ijitcs.2016.10.05.

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10

Farzizadeh, Mohammad, and Ali Abdolahi. "Clustering Students By K-means." International Journal of Computer Applications Technology and Research 5, no. 8 (July 26, 2016): 530–32. http://dx.doi.org/10.7753/ijcatr0508.1006.

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11

Saxena, Divyanshu, Akriti Singh, Aakash Vishwakarma, Devanshi Srivastava, and Shilpa Singhal. "Customer Clustering Using K-Means." Journal of Innovations in Data Science and Big Data Management 1, no. 2 (June 13, 2022): 14–21. http://dx.doi.org/10.46610/jidsbdm.2022.v01i02.003.

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Customer Segmentation analyzes customers interacting with the company and its product. Here, behavior depends on the money they spend and the frequency of buying. To grow a business efficiently, it is essential to analyze the market’s competition and identify customer patterns timely. To do so, customers are divided into groups with different characteristics, like customers who spend more money and are more frequent those who spend less money and are less frequent than others. Using the above data, companies can then outperform the competition in the market by developing products and services according to customer needs. The above analysis is done using the “k-means” clustering, an unsupervised machine learning algorithm. K-means divides customer data into different clusters based on their spending habits.

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Liu, Bowen, Ting Zhang, Yujian Li, Zhaoying Liu, and Zhilin Zhang. "Kernel Probabilistic K-Means Clustering." Sensors 21, no. 5 (March 8, 2021): 1892. http://dx.doi.org/10.3390/s21051892.

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

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Rengasamy, Sundar, and Punniyamoorthy Murugesan. "K-means – Laplacian clustering revisited." Engineering Applications of Artificial Intelligence 107 (January 2022): 104535. http://dx.doi.org/10.1016/j.engappai.2021.104535.

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14

Wakhidah, Nur. "CLUSTERING MENGGUNAKAN K-MEANS ALGORITHM." Jurnal Transformatika 8, no. 1 (July 30, 2010): 33. http://dx.doi.org/10.26623/transformatika.v8i1.45.

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Klasifikasi adalah proses pengorganisasian objek ke dalam kelompok yang anggotanya adalah sama dalam cara yang sama dan merupakan bagian dari pengenalan pola. Dua jenis pengklasifikasian adalah klasifikasi supervised dan klasifikasi unsupervised. K-means adalah jenis metode klasifikasi tak terawasi (unsupervised) yang partisi item data ke dalam satu atau lebih cluster. K-means mencoba untuk memodelkan dataset ke dalam kelompok sehingga item data yang di cluster memiliki karakteristik yang sama dan memiliki karakteristik yang berbeda dari kelompok lainnya.

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15

Liu, Yue, and Bufang Li. "Bayesian hierarchical K-means clustering." Intelligent Data Analysis 24, no. 5 (September 30, 2020): 977–92. http://dx.doi.org/10.3233/ida-194807.

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Clustering algorithm is the foundation and important technology in data mining. In fact, in the real world, the data itself often has a hierarchical structure. Hierarchical clustering aims at constructing a cluster tree, which reveals the underlying modal structure of a complex density. Due to its inherent complexity, most existing hierarchical clustering algorithms are usually designed heuristically without an explicit objective function, which limits its utilization and analysis. K-means clustering, the well-known simple yet effective algorithm which can be expressed from the view of probability distribution, has inherent connection to Mixture of Gaussians (MoG). At this point, we consider combining Bayesian theory analysis with K-means algorithm. This motivates us to develop a hierarchical clustering based on K-means under the probability distribution framework, which is different from existing hierarchical K-means algorithms processing data in a single-pass manner along with heuristic strategies. For this goal, we propose an explicit objective function for hierarchical clustering, termed as Bayesian hierarchical K-means (BHK-means). In our method, a cascaded clustering tree is constructed, in which all layers interact with each other in the network-like manner. In this cluster tree, the clustering results of each layer are influenced by the parent and child nodes. Therefore, the clustering result of each layer is dynamically improved in accordance with the global hierarchical clustering objective function. The objective function is solved using the same algorithm as K-means, the Expectation-maximization algorithm. The experimental results on both synthetic data and benchmark datasets demonstrate the effectiveness of our algorithm over the existing related ones.

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Salman, Raied, Vojislav Kecman, Qi Li, Robert Strack, and Erik Test. "Fast K-Means Algorithm Clustering." International journal of Computer Networks & Communications 3, no. 4 (July 31, 2011): 17–31. http://dx.doi.org/10.5121/ijcnc.2011.3402.

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17

Statman, Adiel, Liat Rozenberg, and Dan Feldman. "k-Means: Outliers-Resistant Clustering+++." Algorithms 13, no. 12 (November 27, 2020): 311. http://dx.doi.org/10.3390/a13120311.

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The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a provably small approximation error in time that is linear in n. We generalize k-means++ to support outliers in two sense (simultaneously): (i) nonmetric spaces, e.g., M-estimators, where the distance dist(p,x) between a point p and a center x is replaced by mindist(p,x),c for an appropriate constant c that may depend on the scale of the input. (ii) k-means clustering with m≥1 outliers, i.e., where the m farthest points from any given k centers are excluded from the total sum of distances. This is by using a simple reduction to the (k+m)-means clustering (with no outliers).

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18

Galluccio, Laurent, Olivier Michel, Pierre Comon, and Alfred O. Hero. "Graph based k-means clustering." Signal Processing 92, no. 9 (September 2012): 1970–84. http://dx.doi.org/10.1016/j.sigpro.2011.12.009.

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19

Chao, Guoqing. "Discriminative K-Means Laplacian Clustering." Neural Processing Letters 49, no. 1 (March 23, 2018): 393–405. http://dx.doi.org/10.1007/s11063-018-9823-7.

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20

Sinaga, Kristina P., and Miin-Shen Yang. "Unsupervised K-Means Clustering Algorithm." IEEE Access 8 (2020): 80716–27. http://dx.doi.org/10.1109/access.2020.2988796.

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21

Janßen, Anja, and Phyllis Wan. "$k$-means clustering of extremes." Electronic Journal of Statistics 14, no. 1 (2020): 1211–33. http://dx.doi.org/10.1214/20-ejs1689.

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22

Alguwaizani, Abdulrahman. "Degeneracy on K-means clustering." Electronic Notes in Discrete Mathematics 39 (December 2012): 13–20. http://dx.doi.org/10.1016/j.endm.2012.10.003.

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23

Kannammal A, Sindhu P, Santhiya R, Sujitha S, and Yuvetha S. "FRACTIONATION USING K MEANS CLUSTERING." international journal of engineering technology and management sciences 7, no. 2 (2023): 740–48. http://dx.doi.org/10.46647/ijetms.2023.v07i02.079.

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The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, is common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.

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24

Xiao, Yu, and Jian Yu. "Partitive clustering (K -means family)." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 3 (March 23, 2012): 209–25. http://dx.doi.org/10.1002/widm.1049.

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25

Muhammad Tianda, Izhar, Mohammad Noufal Ubadah, M. Fariz Fadillah Mardianto, Said Agil Al Munawwarah, Nurhalisa Ishak, Dita Amelia, and Elly Ana. "Clustering Fake News with K-Means and Agglomerative Clustering Based on Word2Vec." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 12, no. 02 (February 4, 2024): 3999–4007. http://dx.doi.org/10.47191/ijmcr/v12i2.01.

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Fake News on digital platforms is a major problem in this digital age. Many people want to find methods to detect Fake News. This research looks at a way to group Fake News articles using K-Means and Agglomerative Clustering techniques, using the semantic representations from Word2Vec embeddings. The researchers use natural language translation methods and advanced machine learning to improve the accuracy and efficiency of Fake News detection. The study involves getting meaningful features from textual data, turning them into vector representations using Word2Vec, and then applying clustering algorithms to sort similar articles. The methodology aims to improve the most recent state of the art in Fake News detection, helping to create more reliable and robust tools to fight misinformation in the digital age, In the comparative analysis of clustering metrics, K-Means clustering exhibits a Purity Score of 88.09% and an Adjusted Rand Score of 58.03%. Conversely, Agglomerative Clustering with the Ward method yields a Purity Score of 85.13% and an Adjusted Rand Score of 49.36%.The Purity Score of 88.09% for K-Means suggests a strong ability to form clusters where the majority of data points share the same true class. Agglomerative Clustering with Ward, though slightly lower at 85.13%, also demonstrates effective class separation within clusters. When considering the Adjusted Rand Score, which accounts for chance and measures the agreement between true and predicted labels, K-Means significantly outperforms Agglomerative Clustering with Ward. The scores are 58.03% and 49.36%, respectively

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Pham, D. T., S. S. Dimov, and C. D. Nguyen. "Selection of K in K-means clustering." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 219, no. 1 (January 1, 2005): 103–19. http://dx.doi.org/10.1243/095440605x8298.

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The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.

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Meng, Wang, Dui Hongyan, Zhou Shiyuan, Dong Zhankui, and Wu Zige. "The Kernel Rough K-Means Algorithm." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 234–39. http://dx.doi.org/10.2174/2213275912666190716121431.

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Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in recent years. The clustering method can be divided into two categories according to the uncertainty, which are hard clustering and soft clustering. The Hard C-Means clustering (HCM) belongs to hard clustering while the Fuzzy C-Means clustering (FCM) belongs to soft clustering in the field of k-means clustering research respectively. The linearly separable problem is a big challenge to clustering and classification algorithm and further improvement is required in big data era. Objective: RKM algorithm based on fuzzy roughness is also a hot topic in current research. The rough set theory and the fuzzy theory are powerful tools for depicting uncertainty, which are the same in essence. Therefore, RKM can be kernelized by the mean of KFCM. In this paper, we put forward a Kernel Rough K-Means algorithm (KRKM) for RKM to solve nonlinear problem for RKM. KRKM expanded the ability of processing complex data of RKM and solve the problem of the soft clustering uncertainty. Methods: This paper proposed the process of the Kernel Rough K-Means algorithm (KRKM). Then the clustering accuracy was contrasted by utilizing the data sets from UCI repository. The experiment results shown the KRKM with improved clustering accuracy, comparing with the RKM algorithm. Results: The classification precision of KFCM and KRKM were improved. For the classification precision, KRKM was slightly higher than KFCM, indicating that KRKM was also an attractive alternative clustering algorithm and had good clustering effect when dealing with nonlinear clustering. Conclusion: Through the comparison with the precision of KFCM algorithm, it was found that KRKM had slight advantages in clustering accuracy. KRKM was one of the effective clustering algorithms that can be selected in nonlinear clustering.

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Smyrlis, P. N., D. C. Tsouros, and M. G. Tsipouras. "Constrained K-Means Classification." Engineering, Technology & Applied Science Research 8, no. 4 (August 18, 2018): 3203–8. http://dx.doi.org/10.48084/etasr.2149.

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Classification-via-clustering (CvC) is a widely used method, using a clustering procedure to perform classification tasks. In this paper, a novel K-Means-based CvC algorithm is presented, analysed and evaluated. Two additional techniques are employed to reduce the effects of the limitations of K-Means. A hypercube of constraints is defined for each centroid and weights are acquired for each attribute of each class, for the use of a weighted Euclidean distance as a similarity criterion in the clustering procedure. Experiments are made with 42 well–known classification datasets. The experimental results demonstrate that the proposed algorithm outperforms CvC with simple K-Means.

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Pokharel, Manoj, Jagdish Bhatta, and Nawaraj Paudel. "Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering." NUTA Journal 8, no. 1-2 (December 31, 2021): 79–87. http://dx.doi.org/10.3126/nutaj.v8i1-2.44044.

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Clustering in data mining is a way of organizing a set of objects in such a way that the objects in same bunch are more comparable and relevant to each other than to those objects in other bunches. In the modern information retrieval system, clustering algorithms are better if they result high quality clusters in efficient time. This study includes analysis of clustering algorithms k-means and enhanced k-means algorithm over the wholesale customers and wine data sets respectively. In this research, the enhanced k-means algorithm is found to be 5% faster for wholesale customers dataset for 4 clusters and 49%, 38% faster when the clusters size is increased to 8 and 13 respectively. The wholesale customers dataset when classified with 18 clusters the speedup was seen to be 29%. Similarly, in the case of wine dataset, the speed up is seen to be 10%, 30%, 49%, and 41% for 3, 8, 13 and 18 clusters respectively. Both of the algorithms are found very similar in terms of the clustering accuracy.

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Simoes, Stanley, Deepak P, and Muiris MacCarthaigh. "Towards Fairer Centroids in K-means Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21583–91. http://dx.doi.org/10.1609/aaai.v38i19.30156.

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There has been much recent interest in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and sex. Within the centroid clustering paradigm, these algorithms are seen to generate clusterings where different groups are disadvantaged within different clusters with respect to their representativity, i.e., distance to centroid. In view of this deficiency, we propose a novel notion of cluster-level centroid fairness that targets the representativity unfairness borne by groups within each cluster, along with a metric to quantify the same. Towards operationalising this notion, we draw on ideas from political philosophy aligned with consideration for the worst-off group to develop Fair-Centroid; a new clustering method that focusses on enhancing the representativity of the worst-off group within each cluster. Our method uses an iterative optimisation paradigm wherein an initial cluster assignment is refined by reassigning objects to clusters such that the worst-off group in each cluster is benefitted. We compare our notion with a related fairness notion and show through extensive empirical evaluations on real-world datasets that our method significantly enhances cluster-level centroid fairness at low impact on cluster coherence.

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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 (September 15, 2014): 15851–56. http://dx.doi.org/10.15680/ijirset.2014.0309009.

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32

Trenggonowati, D. L., M. Ulfah, R. Ekawati, and V. A. Yusuf. "Organization clustering airports using K-Means clustering algorithm." IOP Conference Series: Materials Science and Engineering 673 (December 10, 2019): 012081. http://dx.doi.org/10.1088/1757-899x/673/1/012081.

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33

Jiang, Hua, Shenghe Yi, Jing Li, Fengqin Yang, and Xin Hu. "Ant clustering algorithm with K-harmonic means clustering." Expert Systems with Applications 37, no. 12 (December 2010): 8679–84. http://dx.doi.org/10.1016/j.eswa.2010.06.061.

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Mohd, Wan Maseri Binti Wan, A. H. Beg, Tutut Herawan, A. Noraziah, and K. F. Rabbi. "Improved Parameterless K-Means." International Journal of Information Retrieval Research 1, no. 3 (July 2011): 1–14. http://dx.doi.org/10.4018/ijirr.2011070101.

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K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points.

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Komori, Osamu, and Shinto Eguchi. "A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average." Entropy 23, no. 5 (April 24, 2021): 518. http://dx.doi.org/10.3390/e23050518.

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Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.

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Qona'ah, Niswatul, Alvita Rachma Devi, and I. Made Gde Meranggi Dana. "Laboratory Clustering using K-Means, K-Medoids, and Model-Based Clustering." Indonesian Journal of Applied Statistics 3, no. 1 (July 23, 2020): 64. http://dx.doi.org/10.13057/ijas.v3i1.40823.

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<p>Institut Teknologi Sepuluh Nopember (ITS) is an institute which has about 100 laboratories to support some academic activity like teaching, research and society service. This study is clustering the laboratory in ITS based on the productivity of laboratory in carrying out its function. The methods used to group laboratory are <em>K</em>-Means, <em>K</em>-Medoids, and Model-Based Clustering. <em>K</em>-Means and <em>K</em>-Medoids are non-hierarchy clustering method that the number of cluster can be given at first. The difference of them is datapoints that selected by <em>K</em>-Medoids as centers (medoids or exemplars) and mean for <em>K</em>-Means. Whereas, Model-Based Clustering is a clustering method that takes into account statistical models. This statistical method is quite developed and considered to have advantages over other classical method. Comparison of each cluster method using Integrated Convergent Divergent Random (ICDR). The best method based on ICDR is Model-Based Clustering.</p><p><strong>Keywords</strong><strong> : </strong><em>K</em>-Means, <em>K</em>-Medoids, Laboratory, Model-Based Clustering</p>

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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 (April 1, 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 as compard to K-means clustering in image segmentation. </p>

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Balakumar, Arvind. "Quantum K-means Clustering and Classical k Means Clustering For Chest Pain Classification Using Qiskit." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 945–48. http://dx.doi.org/10.22214/ijraset.2022.47484.

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Abstract: Quantum computing is an upcoming field of technology which has a broad scope of increasing the current technology in a robust manner. The application area Quantum computing is very huge which ranges from battery research, protein structure research to advance computing and security areas like cryptography, quantum internet, quantum machine learning and quantum cyber security. The quantum machine learning area seems to be the most interesting because of the computing capability of a real time quantum computer. With the quantum machine learning algorithm, classical algorithm, processing speed of current quantum computers and available data in modern world we could gain insights and find patterns which were previously not possible at all.

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Zhang, Yi, Xinwang Liu, Jiyuan Liu, Sisi Dai, Changwang Zhang, Kai Xu, and En Zhu. "Fusion Multiple Kernel K-means." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 9109–17. http://dx.doi.org/10.1609/aaai.v36i8.20896.

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Multiple kernel clustering aims to seek an appropriate combination of base kernels to mine inherent non-linear information for optimal clustering. Late fusion algorithms generate base partitions independently and integrate them in the following clustering procedure, improving the overall efficiency. However, the separate base partition generation leads to inadequate negotiation with the clustering procedure and a great loss of beneficial information in corresponding kernel matrices, which negatively affects the clustering performance. To address this issue, we propose a novel algorithm, termed as Fusion Multiple Kernel k-means (FMKKM), which unifies base partition learning and late fusion clustering into one single objective function, and adopts early fusion technique to capture more sufficient information in kernel matrices. Specifically, the early fusion helps base partitions keep more beneficial kernel details, and the base partitions learning further guides the generation of consensus partition in the late fusion stage, while the late fusion provides positive feedback on two former procedures. The close collaboration of three procedures results in a promising performance improvement. Subsequently, an alternate optimization method with promising convergence is developed to solve the resultant optimization problem. Comprehensive experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance on multiple public datasets, validating its effectiveness. The code of this work is publicly available at https://github.com/ethan-yizhang/Fusion-Multiple-Kernel-K-means.

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Akbari, Gumilar, and Yusrila Kerlooza. "Peningkatan Hasil Cluster Menggunakan Algoritma Dynamic K-means dan K-means Binary Search Centroid." Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi 4, no. 1 (April 9, 2018): 25–33. http://dx.doi.org/10.34010/jtk3ti.v4i1.1395.

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Pada studi kasus segmentasi pelanggan, data yang digunakan untuk segmentasi memiliki atribut data berdasarkan nilai Recency, Frequency, dan Monetery dan memiliki jumlah data 500, untuk membentuk segmentasi pelanggan dapat digunakan teknik clustering. Clustering adalah proses untuk mengelompokkan datum ke dalam sejumlah cluster (kelompok data). Salah satu teknik Clustering adalah teknik clustering partisi, algoritma clustering yang digunakan pada penelitian ini yaitu algoritma Dynamic K-means (DK) dan K-means Binary Search Centroid (KBSC). Pada algoritma Dynamic K-means memiliki kemampuan untuk mencari jumlah Cluster, namun memiliki kekurangan dalam penentuan titik centroid (pusat cluster), sedangkan algoritma KBSC memiliki kemampuan untuk menentukan titik centroid Cluster, namun memiliki kekurangan dalam mencari jumlah Cluster. Pada penelitian ini menggabungkan kedua algoritma antara algoritma DK dan KBSC dan akan diujikan pada data model buatan yang bertujuan untuk melihat karakteristik dari algoritma, dan diujikan pada data studi studi kasus yang bertujuan untuk mengetahui kemampuan algoritma dalam menyelasaikan kasus segmentasi pelanggan. Berdasarkan pengukuran Devies Bouldin Index (DBI) algoritma gabungan DK-KBSC menghasilkan nilai DBI lebih baik dibandingkan algoritma lainnya.saat diimplementasikan pada data kasus segmentasi pelanggan.

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Hämäläinen, Joonas, Tommi Kärkkäinen, and Tuomo Rossi. "Improving Scalable K-Means++." Algorithms 14, no. 1 (December 27, 2020): 6. http://dx.doi.org/10.3390/a14010006.

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Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation algorithm is given. The experiments show that the proposed methods compare favorably to the state-of-the-art by improving clustering accuracy and the speed of convergence. We also observe that the currently most popular K-means++ initialization behaves like the random one in the very high-dimensional cases.

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Pham, D. T., S. S. Dimov, and C. D. Nguyen. "An Incremental K-means algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 7 (July 1, 2004): 783–95. http://dx.doi.org/10.1243/0954406041319509.

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Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency. The main problem with this clustering method is its tendency to coverge at a local minimum. In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined. The jumping technique alleviates the problem with local minima by enabling cluster centres to move in such a radical way as to reduce the overall cluster distortion. However, the method is very sensitive to errors in estimating distortion. A clustering scheme that is also based on distortion reduction through cluster centre movement but is not so sensitive to inaccuracies in distortion estimation is proposed in this paper. The scheme, which is an incremental version of the K-means algorithm, involves adding cluster centres one by one as clusters are being formed. The paper presents test results to demonstrate the efficacy of the proposed algorithm.

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Cheung, Yiu-Ming. "-Means: A new generalized k-means clustering algorithm." Pattern Recognition Letters 24, no. 15 (November 2003): 2883–93. http://dx.doi.org/10.1016/s0167-8655(03)00146-6.

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Terada, Yoshikazu. "Strong consistency of factorial $$K$$ K -means clustering." Annals of the Institute of Statistical Mathematics 67, no. 2 (March 27, 2014): 335–57. http://dx.doi.org/10.1007/s10463-014-0454-0.

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Moradi Fard, Maziar, Thibaut Thonet, and Eric Gaussier. "Deep k-Means: Jointly clustering with k-Means and learning representations." Pattern Recognition Letters 138 (October 2020): 185–92. http://dx.doi.org/10.1016/j.patrec.2020.07.028.

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Hong, Dong-Kweon. "NoSQL Database Partition using K-means Clustering." Journal of Korean Institute of Intelligent Systems 31, no. 5 (October 31, 2021): 416–21. http://dx.doi.org/10.5391/jkiis.2015.25.3.416.

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Jee, Tae-Chang, Hyun-Jin Lee, and Yill-Byung Lee. "Fast K-Means Clustering Algorithm using Prediction Data." Journal of the Korea Contents Association 9, no. 1 (January 28, 2009): 106–14. http://dx.doi.org/10.5392/jkca.2009.9.1.106.

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Gao, Zhiqiang, Yixiao Sun, Xiaolong Cui, Yutao Wang, Yanyu Duan, and Xu An Wang. "Privacy-Preserving Hybrid K-Means." International Journal of Data Warehousing and Mining 14, no. 2 (April 2018): 1–17. http://dx.doi.org/10.4018/ijdwm.2018040101.

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

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Yadav, Nisha, Ambuja Salgaonkar, and Mayank Vahia. "Clustering Indus Texts using K-means." International Journal of Computer Applications 162, no. 1 (March 15, 2017): 16–21. http://dx.doi.org/10.5120/ijca2017913207.

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Charansiriphaisan, Kanjana, Dr Sirapat Chiewchanwattana, and Kamron Sunut. "K - Inverse Harmonic Means Clustering Algorithm." Khon Kaen University Journal (Graduate Studies) 11, no. 2 (April 1, 2011): 21–30. http://dx.doi.org/10.5481/kkujgs.2011.11.2.3.

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