Clustering

Counting Clusters

March 13, 2011 | Edwin Chen

Given a set of numerical datapoints, we often want to know how many clusters the datapoints form. Two practical algorithms for determining the number of clusters are the gap statistic and the prediction strength. Gap Statistic The gap statistic algorithm … Continue reading →
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web content anlayzer

January 6, 2011 | Martin Scharm

Just developed a small crawler to check my online content at binfalse.de in terms of W3C validity and the availability of external links. Here is the code and some statistics... [Read more...]

Clustergram: visualization and diagnostics for cluster analysis (R code)

June 15, 2010 | Tal Galili

About Clustergrams In 2002, Matthias Schonlau published in “The Stata Journal” an article named “The Clustergram: A graph for visualizing hierarchical and . As explained in the abstract: In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. I propose an alternative graph named “clustergram” to examine how ... [Read more...]

Top 10 Algorithms in Data Mining

April 23, 2010 | Stephen Turner

The authors here invited ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining, including the algorithm name, justification for nomination, and a representative public... [Read more...]

Augmented support for complex survey designs in R

March 3, 2010 | Nick Horton

We'll get back to code examples later this week, but wanted to let you know about an R package with updated functionality in the meantime.The appropriate analysis of sample surveys requires incorporation of complex design features, including stratification, clustering, weights, and finite population correction. These can be address in ...
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Machine Learning in R

September 10, 2009 | Stephen Turner

Revolutions blog recently posted a link to R code by Joshua Reich with self-contained examples of using machine learning techniques in R, including various clustering methods (k-means, nearest neighbor, and kernel), recursive partitioning (CART), principle components analysis, linear discriminant analysis, and support vector machines.  This post also links to some ... [Read more...]

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