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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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Robustness Properties of k Means and Trimmed k Means
Journal of the American Statistical Association, 1999Luis Angel Garcia-Escudero +1 more
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IO-K-Means: Iterative Optimization for Centroids in K-Means
Shuntai Zhang +6 moreopenaire +1 more source
Bengali Document Clustering: A Comparative Study of K-Means, K-Means++, Spectral K-Means
Amartya Roy +2 moreopenaire +1 more source