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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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In-Memory Parallel Processing of Massive Remotely Sensed Data Using an Apache Spark on Hadoop YARN Model
TL;DR: Experiments indicated that the Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image tookLess than 260 s.
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Mixtures of multivariate power exponential distributions.
TL;DR: An expanded family of mixtures of multivariate power exponential distributions is introduced and a family of parsimonious models is proposed using an eigen‐decomposition of the scale matrix to deal with both varying tail‐weight and peakedness of data.
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Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction
Arne De Coninck,Bernard De Baets,Drosos Kourounis,Fabio Verbosio,Olaf Schenk,Steven Maenhout,Jan Fostier +6 more
TL;DR: It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework, and it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data.
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Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes
TL;DR: This paper extends the applicability of a previously proposed class of dynamic space-time models by enabling them to accommodate large datasets by using a low-rank predictive process to reduce the dimensionality of the data and ease the computational burden of estimating the spatio-temporal process of interest.
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Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
TL;DR: A technique for transferring knowledge from heterogeneous external sources, including image and textual data, to facilitate three related tasks in understanding video emotion: emotion recognition, emotion attribution and emotion-oriented summarization is proposed.
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