Results 261 to 270 of about 15,644,966 (296)

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), 2016
K-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, Yanwei Yu, Lihong Wang, Jinglei Liu   +3 more
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Implementation of K-means algorithm on FGGA

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
K-means algorithm is one of the clustering algorithms that increase in popularity day by day. The intensive mathematical operations and the continuous increase of the data size while clustering on large data using the K-means algorithm prevent the algorithm from operating at high performance.
Cavuslu, Mehmet Ali, Turkoglu, Bahadir, ALTUNCU, MEHMET ALİ, ŞAHİN, SUHAP   +3 more
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Noise identification with the k-means algorithm

16th IEEE International Conference on Tools with Artificial Intelligence, 2005
The presence of noise in a measurement dataset can have a negative effect on the classification model built. More specifically, the noisy instances in the dataset can adversely affect the learnt hypothesis. Removal of noisy instances will improve the learnt hypothesis; thus, improving the classification accuracy of the model.
Wei Tang, Taghi M. Khoshgoftaar
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Capped Robust K-means Algorithm

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017
K-means algorithm is a classical algorithm and has been widely used in many applications. However, the traditional K-means algorithm is easily influenced by outliers and it usually obtains an unstable clustering result and poor clustering accuracy. In this paper, aiming at K-means algorithm resistant to outliers, we proposed a Capped Robust K-means ...
Ting Zhang, Fang Yuan, Liu Yang
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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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Two Improved k-Means Algorithms

Applied Soft Computing Journal, 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.
Yung-Kuan Chan, Ting-Cheng Chang
exaly   +3 more sources

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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