Results 271 to 280 of about 559,125 (302)

A Modified Version of K-Means Algorithm

2021
In this work is presented a modified version of the K-Means which identifies cluster stability. The stability is defined by a threshold based on a percentage of the largest displacement of centroid at first iteration. A cluster is considered stable when the largest centroid displacement in the current iteration achieves the 10% of threshold, and ...
Adriana Mexicano, Jesús Carlos Carmona, Salvador Cervantes, Jose-Antonio Cervantes, Sonia López, Ricardo Rodríguez Jorge   +5 more
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An Efficient k-Means Algorithm on CUDA

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, 2011
The $k$-means algorithm is widely used for unsupervised clustering. This paper describes an efficient CUDA-based $k$-means algorithm. Different from existing GPU-based k-means algorithms, our algorithm achieves better efficiency by utilizing the triangle inequality.
Jiadong Wu, Bo Hong
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An Improved K-means Clustering Algorithm

2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), 2020
Among the existing clustering algorithms, K-means algorithm has become one of the most widely used technologies, mainly because of its simplicity and effectiveness. However, the selection of the initial clustering centers and the sensitivity to noise will reduce the clustering effect.
Hui Xu, Shunyu Yao, Qianyun Li, Zhiwei Ye   +3 more
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-Means: A new generalized k-means clustering algorithm

Pattern Recognition Letters, 2003
Summary: 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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Asymmetric k-Means Algorithm

2011
In this paper, an asymmetric version of the k-means clustering algorithm is proposed. The asymmetry arises caused by the use of asymmetric dissimilarities in the k-means algorithm. Application of asymmetric measures of dissimilarity is motivated with a basic nature of the k-means algorithm, which uses dissimilarities in an asymmetric manner.
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An Enhancement of K-means Clustering Algorithm

2009 International Conference on Business Intelligence and Financial Engineering, 2009
Abstract—K-means clustering algorithm and one of its Enhancements are studied in this paper. Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance ...
Jirong Gu, Jieming Zhou, Xianwei Chen
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A note on constrained k-means algorithms

Pattern Recognition, 2000
Abstract This paper describes extensions to the k -means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k -means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster.
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Two improved k-means algorithms

Applied Soft Computing, 2018
Abstract K-means algorithm is the most commonly used simple clustering method. For a large number of high dimensional numerical data, it provides an efficient method for classifying similar data into the same cluster. In this study, a tri-level k-means algorithm and a bi-layer k-means algorithm are proposed.
Shyr-Shen Yu, Shao Wei Chu, Chuin-Mu Wang, Yung-Kuan Chan, Ting-Cheng Chang   +4 more
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Elkan’s k-Means Algorithm for Graphs

2010
This paper proposes a fast k-means algorithm for graphs based on Elkan's k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric.
Brijnesh J. Jain, Klaus Obermayer
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The MinMax k-Means clustering algorithm

Pattern Recognition, 2014
Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and
Grigorios Tzortzis, Aristidis Likas
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