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Category Archives: Clustering
K-modes
I recently read an interesting Wired story about Chris McKinlay (a fellow alum of Middlebury College), who used a clustering algorithm to understand the pool of users on the dating site OkCupid (and successfully used this information to improve his … Continue reading
Posted in Clustering, Feature extraction
16 Comments
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
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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
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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
