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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
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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
6 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