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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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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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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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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 International Joint Conference on Neural Networks (IJCNN), 2020
Recently, more researchers are interested in the domain of quantum machine learning as it can manipulate and classify large numbers of vectors in high dimensional space in reasonable time.In this paper, we propose a new approach called Quantum Collaborative K-means which is based on combining several clustering models based on quantum K-means.
Kaoutar Benlamine +3 more
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Recently, more researchers are interested in the domain of quantum machine learning as it can manipulate and classify large numbers of vectors in high dimensional space in reasonable time.In this paper, we propose a new approach called Quantum Collaborative K-means which is based on combining several clustering models based on quantum K-means.
Kaoutar Benlamine +3 more
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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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Computational Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena +2 more
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2011 IEEE 11th International Conference on Data Mining Workshops, 2011
The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered ...
Giuseppe Di Fatta +3 more
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The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered ...
Giuseppe Di Fatta +3 more
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-Means: A new generalized k-means clustering algorithm
Pattern Recognition Letters, 2003Summary: This paper presents a generalized version of the conventional \(k\)-means clustering algorithm. Not only is this new one applicable to ellipse-shaped data clusters without dead-unit problem, but also performs correct clustering without pre-assigning the exact cluster number.
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An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis
Pattern Recognition, 2023Haize Hu, Jianxun Liu
exaly