
Recent Posts
Recent Comments
Max Moroz on Gaussian kernels Popular DS, AI, ML B… on Kmodes TuringBot (@turing_b… on Genetic algorithms and symboli… ct98.aspx on Random forests Paul on Kernels Archives
 December 2016
 November 2016
 October 2016
 June 2016
 April 2016
 January 2016
 November 2015
 October 2015
 July 2015
 June 2015
 May 2015
 January 2015
 September 2014
 June 2014
 May 2014
 March 2014
 February 2014
 January 2014
 December 2013
 October 2013
 September 2013
 August 2013
 July 2013
 June 2013
 May 2013
 April 2013
 March 2013
Categories
Meta
Category Archives: Unsupervised learning
Modularity – Measuring cluster separation
We’ve now seen a number of different clustering algorithms, each of which will divide a data set into a number of subsets. This week, I want to ask the question: How do we know if answer that a clustering algorithm … Continue reading
Posted in Clustering, Unsupervised learning
Leave a comment
Spectral clustering
In the last few posts, we’ve been studying clustering, i.e. algorithms that try to cut a given data set into a number of smaller, more tightly packed subsets, each of which might represent a different phenomenon or a different type … Continue reading
Posted in Clustering, Unsupervised learning
9 Comments
Mapper and the choice of scale
In last week’s post, I described the DBSCAN clustering algorithm, which uses the notion of density to determine which data points in a data set form tightly packed groups called clusters. This algorithm relies on two parameters – a distance … Continue reading
Posted in Clustering, Unsupervised learning
4 Comments
Clusters and DBScan
A few weeks ago, I mentioned the idea of a clustering algorithm, but here’s a recap of the idea: Often, a single data set will be made up of different groups of data points, each of which corresponds to a … Continue reading
Posted in Clustering, Unsupervised learning
7 Comments
Intrinsic vs. Extrinsic Structure
At this point, I think it will be useful to introduce an idea from geometry that is very helpful in pure mathematics, and that I find helpful for understanding the geometry of data sets. This idea is difference between the … Continue reading
Posted in Unsupervised learning
9 Comments
Kmeans
The subject of this weeks post is probably one of the most polarizing algorithms in the data world: It seems that most experts either swear by Kmeans or absolutely hate it. The difference of opinion boils down to one of … Continue reading
Posted in Modeling, Unsupervised learning
8 Comments