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k*-Means — A Generalized k-Means Clustering Algorithm with Unknown Cluster Number
2002This paper presents a new clustering technique named STepwise Automatic Rival-penalized (STAR) k-means algorithm (denoted as k*-means), which is actually a generalized version of the conventional k-means (MacQueen 1967). Not only is this new algorithm applicable to ellipse-shaped data clusters rather than just to ball-shaped ones like the k-means ...
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2009
Clustering techniques are used for finding suitable groupings of samples belonging to a given set of data. There is no knowledge a priori about these data. Therefore, such set of samples cannot be considered as a training set, and classification techniques cannot be used in this case.
Antonio Mucherino +2 more
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Clustering techniques are used for finding suitable groupings of samples belonging to a given set of data. There is no knowledge a priori about these data. Therefore, such set of samples cannot be considered as a training set, and classification techniques cannot be used in this case.
Antonio Mucherino +2 more
openaire +1 more source
Collaborative annealing power k-means++ clustering
Knowledge-Based Systems, 2022Hongzong LI, Jun Wang
exaly
The global k-means clustering algorithm
Pattern Recognition, 2003Aristidis Likas, Nikos Vlassis
exaly
Automated variable weighting in k-means type clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Joshua Huang, Michael Ng
exaly
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering, 2007Michael Ng, Joshua Huang
exaly
Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm
Procedia Computer Science, 2015Nameirakpam Dhanachandra
exaly
TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data
IEEE Transactions on Knowledge and Data Engineering, 2013Xiaojun Chen, Yunming Ye, Joshua Huang
exaly
K-Means Clustering Versus Validation Measures: A Data-Distribution Perspective
IEEE Transactions on Systems, Man, and Cybernetics, 2009Junjie Wu, Jian Chen
exaly

