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Fuzzy K-Means Clustering With Discriminative Embedding
IEEE Transactions on Knowledge and Data Engineering, 2022Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function.
F. Nie +4 more
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2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
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A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
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Dynamic Coverage Control Based on K-Means
IEEE transactions on industrial electronics (1982. Print), 2022In this article, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires
Dengxiu Yu +4 more
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K*-Means: An Effective and Efficient K-Means Clustering Algorithm
2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi +3 more
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The impact of neglecting feature scaling in k-means clustering
PLoS ONEDespite the popularity of k-means clustering, feature scaling before applying it can be an essential yet often neglected step. In this study, feature scaling via five methods: Z-score, Min-Max normalization, Percentile transformation, Maximum absolute ...
Chantha Wongoutong
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2017
K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++
K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.Aliguliyev, Ramiz, Shalala F. Tahirzada
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, 2020
Clustering is an explorative data analysis technique used for investigating the underlying structure in the data. It described as the grouping of objects, where the objects share similar characteristics. Over the past 50 years, clustering has been widely
P. Govender, V. Sivakumar
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Clustering is an explorative data analysis technique used for investigating the underlying structure in the data. It described as the grouping of objects, where the objects share similar characteristics. Over the past 50 years, clustering has been widely
P. Govender, V. Sivakumar
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A Modified K-Means Algorithm - Two-Layer K-Means Algorithm
2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another.
Chen-Chung Liu +3 more
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