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The impact of neglecting feature scaling in k-means clustering. [PDF]

PLoS One
Wongoutong C.
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Cartesian K-Means

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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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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Balanced k-Means

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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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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A Modified K-Means Algorithm - Two-Layer K-Means Algorithm

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014
In 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, Shao Wei Chu, Yung-Kuan Chan, Shyr-Shen Yu   +3 more
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Generalized Reduced K–Means

Computational Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena, Rocci, Roberto, Vichi, Maurizio   +2 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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Spherical k-Means++ Clustering

2015
k-means clustering (KM) algorithm, also called hard c-means clustering (HCM) algorithm, is a very powerful clustering algorithm [1, 2], but it has a serious problem of strong initial value dependence. To decrease the dependence, Arthur and Vassilvitskii proposed an algorithm of k-means++ clustering (KM++) algorithm on 2007 [3].
Yasunori Endo, Sadaaki Miyamoto
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An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis

Pattern Recognition, 2023
Haize Hu, Jianxun Liu
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