Journal ArticleDOI
A K-Means Clustering Algorithm
J. A. Hartigan,M. A. Wong +1 more
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This article is published in Journal of The Royal Statistical Society Series C-applied Statistics.The article was published on 1979-03-01. It has received 10702 citations till now. The article focuses on the topics: Canopy clustering algorithm & Correlation clustering.read more
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Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration.
TL;DR: This review discusses how different types of local and global shape changes underlie distinct migration modes and mechanical differences between force-generation mechanisms and finishes by speculating on how they may have evolved.
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Spatiotemporal modeling of PM2.5 data with missing values : Application of recent advances in space-time statistics to atmospheric data
TL;DR: A novel feature of the analysis is a variant of the expectation-maximization algorithm to account for missing data that shows, among other things, that a substantial part of the region is in violation of the proposed long-term average standard for PM2.5.
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Modeling one- and two-layer variable bit rate video
Kavitha Chandra,Amy R. Reibman +1 more
TL;DR: A finite-state Markov chain is shown to accurately model one- and two-layer video of all activity levels on a per source basis to capture the source dynamics, including the short-term correlations essential for studying network performance.
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Maximum-likelihood parameter estimation for image ringing-artifact removal
TL;DR: This work presents a maximum-likelihood approach to the ringing-artifact removal problem that employs a parameter estimation method based on the k-means algorithm with the number of clusters determined by a cluster-separation measure.
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An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images
TL;DR: Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed object-based hierarchical method, leading to the conclusion that this method is practically applicable for frequent monitoring.
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