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2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compressive version of K-means (CKM), that estimates cluster centers from a sketch, i.e. from a drastically compressed representation of the training dataset.
Keriven, Nicolas +3 more
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Deep k-Means: Jointly clustering with k-Means and learning representations
Pattern Recognition Letters, 2020 Under consideration at Pattern Recognition ...
Moradi Fard, Maziar +2 more
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k-Means+++: Outliers-Resistant Clustering
, 2020 The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a
Feldman, Dan +5 more
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Scalability of efficient parallel K-Means [PDF]
, 2009 Clustering is defined as the grouping of similar items in a set, and is an important process within the field of data mining. As the amount of data for various applications continues to increase, in terms of its size and dimensionality, it is necessary ...
Giuseppe Di Fatta +3 more
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Unsupervised K-Means Clustering Algorithm
IEEE Access, 2020 The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
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A parametric k-means algorithm [PDF]
Computational Statistics, 2007 The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points of a parametric distribution.
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K−Means Clustering Microaggregation for Statistical Disclosure Control
, 2012 This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more
Abdun Naser Mahmood +5 more
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