Abstract. Learning the number of clusters is a key problem in data clustering. We present dip-means, a novel robust incremental method to learn the number of data clusters that may be used as a wrapper around any iterative clustering algorithm of the k-means family.
We present dip-means, a novel robust incremental method to learn the number of data clusters that can be used as a wrapper around any iterative clustering ...
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Jul 1, 2016 · The proposed algorithm considers each cluster member as an individual viewer and applies a univariate statistic hypothesis test for unimodality ...
Dec 3, 2012 · The proposed algorithm considers each cluster member as an individual 'viewer' and applies a univariate statistic hypothesis test for ...
Learning the number of clusters is a key problem in data clustering. Dip-means [1] is a robust incremental method to learn the number of data clusters that ...
Dip-means is a novel robust incremental method to learn the number of data clusters that can be used as a wrapper around any iterative clustering algorithm ...
FOR ESTIMATING THE NUMBER OF CLUSTERS. Argyris Kalogeratos and Aristidis ... Clustering is very broadly applied, however, the number of clusters k is ...
Dip-means is an incremental clustering algorithm that uses a statistical criterion called dip-dist to decide whether a set of data objects constitutes a ...
The Projected Dip-means Clustering Algorithm | Request PDF
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Learning the number of clusters is a key problem in data clustering. We present dip-means, a novel robust incremental method to learn the number of data ...
In this paper, we propose a skinny method, DP-Dip, to estimate the number of clusters. Different from many popular methods, DP-Dip does not make any assumptions ...