Search results
Results from the WOW.Com Content Network
AOSP contains a Java implementation for k-means. CrimeStat implements two spatial k-means algorithms, one of which allows the user to define the starting locations. ELKI contains k-means (with Lloyd and MacQueen iteration, along with different initializations such as k-means++ initialization) and various more advanced clustering algorithms.
In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.
Description: This image is part of a series of images showing the operation of the k-means algorithm. This is the fourth step (a repetition of the second step) where the data points are associated with their nearest centroids.
This image is part of a series of images showing an example of the operation of the k-means algorithm. This is the third step where the centroids are moved to the average of all the data points. Date: 26 July 2007: Source: Own work: Author: Weston.pace
{{Information |Description=This image is part of an example of the K-means algorithm. This is the first step, where the points and centroids are randomly placed. |Source=self-made |Date=July 26, 2007 |Author= Weston.pace}}
The penultimate College Football Playoff rankings will be released Tuesday. Our projection of how the top 10 will look ahead of championship weekend.
The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay includes simple examples of the EM algorithm such as clustering using the soft k-means algorithm, and emphasizes the variational view of the EM algorithm, as described in Chapter 33.7 of version 7.2 (fourth edition).
Julia contains a k-medoid implementation of the k-means style algorithm (fast, but much worse result quality) in the JuliaStats/Clustering.jl package. KNIME includes a k-medoid implementation supporting a variety of efficient matrix distance measures, as well as a number of native (and integrated third-party) k-means implementations