Results 241 to 250 of about 1,986,530 (277)

Attribute-Based K-Means Algorithm

2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019
Clustering is a method to discover hidden natural structure in a dataset of a phenomenon. In this study, we have extended K-Means algorithm for spatiotemporal dataset by introducing attribute-based mass function to calculate center of mass of cluster instead of calculating geometry-based centroid in the dataset. The proposed modification in traditional
Anand Prakash, Y. S. Chungkham, Mohd. Yousuf Ansari   +2 more
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Improved K-Means Clustering Algorithm

2008 Congress on Image and Signal Processing, 2008
K-means algorithm is widely used in spatial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages:  sensitive to the initial centroid and instability. The improved clustering algorithm referred to the best clustering centriod which is searched during the optimization of clustering ...
Zhe Zhang, Junxi Zhang, Huifeng Xue
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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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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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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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Adaptive K-means clustering algorithm

SPIE Proceedings, 2007
Clustering is a fundamental problem for a great variety fields such as pattern recognition, computer vision. A popular technique for clustering is based on K-means. However, it suffers from the four main disadvantages. Firstly, it is slow and scales poorly on the time.
Hailin Chen, Xiuqing Wu, Junhua Hu
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Modified k-Means Clustering Algorithm

2011
Clustering is the popular unsupervised learning technique of data mining which divide the data into groups having similar objects and used in various application areas. k-Means is the most popular clustering algorithm among all partition based clustering algorithm to partition a dataset into meaningful patterns. k-Means suffers some shortcomings.
Vaishali R. Patel, Rupa G. Mehta
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Factors influencing K means algorithm

International Journal of Computational Systems Engineering, 2013
Clustering is an unsupervised learning technique. K-means is one of the most popular clustering algorithms. K-means requires the number of clusters to be pre-specified. Finding the appropriate number of clusters for a dataset is a trial-and-error process made more difficult by the subjective nature of deciding what constitutes ‘correct’ clustering (Han
Shejuti Khan, S.M. Monzurur Rahman, M. Faysal Tanim, Fizar Ahmed   +3 more
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An efficient enhanced k-means clustering algorithm

Journal of Zhejiang University-SCIENCE A, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fahim, A. M., Salem, A. M., Torkey, F. A., Ramadan, M. A.   +3 more
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Modified K-Means Clustering Algorithm

2008 Congress on Image and Signal Processing, 2008
Performance of iterative clustering algorithms depends highly on the choice of  cluster centers in each step. In this paper we propose an effective algorithm to compute new cluster centers for each iterative step for K-means clustering. This algorithm is based on the optimization formulation of the problem and a novel iterative method.
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