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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Proceedings ArticleDOI
BotFinder: finding bots in network traffic without deep packet inspection
TL;DR: The results show that BotFinder is able to detect bots in network traffic without the need of deep packet inspection, while still achieving high detection rates with very few false positives.
Journal ArticleDOI
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
TL;DR: This work substantiates its points with extensive experiments, using clustering and outlier detection methods with and without index acceleration, and discusses what one can learn from evaluations, whether experiments are properly designed, and what kind of conclusions one should avoid.
Journal ArticleDOI
The k-means clustering technique: General considerations and implementation in Mathematica
TL;DR: This tutorial presents a simple yet powerful data clustering technique, through three different algorithms: the Forgy/Lloyd, algorithm, the MacQueen algorithm and the Hartigan & Wong algorithm, and an implementation in Mathematica.
Journal ArticleDOI
Inverse shade trees for non-parametric material representation and editing
Jason Lawrence,Aner Ben-Artzi,Christopher DeCoro,Wojciech Matusik,Hanspeter Pfister,Ravi Ramamoorthi,Szymon Rusinkiewicz +6 more
TL;DR: An Inverse Shade Tree framework is introduced that provides a general approach to estimating the "leaves" of a user-specified shade tree from high-dimensional measured datasets of appearance, and the ability to reduce multi-gigabyte measured dataset of the Spatially-Varying Bidirectional Reflectance Distribution Function (SVBRDF) into a compact representation that may be edited in real time is demonstrated.
Journal ArticleDOI
On plant detection of intact tomato fruits using image analysis and machine learning methods.
TL;DR: This study aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches.
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