scispace - formally typeset
Journal ArticleDOI

A K-Means Clustering Algorithm

J. A. Hartigan, +1 more
- 01 Mar 1979 - 
- Vol. 28, Iss: 1, pp 100-108
Reads0
Chats0
About
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

Citations
More filters
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

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.
References
More filters
Related Papers (5)