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The authors study the impact of initial centroids on clustering accuracy for unsupervised feature selection. Three metrics are used to rank the features of a ...
Apr 9, 2024 · Motivated from this key idea, the authors study the impact of initial centroids on clustering accuracy for unsupervised feature selection. Three ...
One of the main problems in K-means clustering is setting of initial centroids which can cause misclustering of patterns which affects clustering accuracy.
Title. An Empirical Study on Initializing Centroid in K-Means Clustering for Feature Selection. Authors. Saxena, Amit; Wang, John; Sintunavarat, Wutiphol.
Dec 31, 2020 · An Empirical Study on Initializing Centroid in K-Means Clustering for Feature Selection. Saxena, Amit;Wang, John;Sintunavarat, Wutiphol.
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Motivated from this key idea, the authors study the impact of initial centroids on clustering accuracy for unsupervised feature selection. Three metrics are ...
. ThemainsignificanceofthepaperisthattheK-meansclusteringyieldshigheraccuraciesinmajority ofthesedatasetsusingproposeddensityanddistance ...
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Jun 27, 2022 · In this paper, we focus on the sensitivity of k -means to its initial set of centroids. Since the cluster recovery performance of k -means can ...
Abstract: Initial starting points those generated randomly by K-means often make the clustering results reaching the local optima.