In this continuing series, we explore the NMath Stats functions for performing cluster analysis in .NET. (For previous posts, see here.) The sample data set we’re using classifies 89 single malt scotch whiskies on a five-point scale (0-4) for 12 flavor characteristics. To visualize the data set and clusterings, we make use of the free Microsoft Chart Controls for .NET, which provide a basic set of charts.

In this post, we’ll cluster the scotches based on “similarity” in the original 12-dimensional flavor space using k-means clustering. The k-means clustering method assigns data points into k groups such that the sum of squares from points to the computed cluster centers is minimized. In NMath Stats, class KMeansClustering performs k-means clustering.
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Cluster analysis is the assignment of a set of objects into one or more clusters based on object similarity. NMath Stats includes a variety of techniques for performing cluster analysis, which we will explore in a series of posts.

The Data Set

The data set we’ll use was created by David Wishart (2002), who classified 89 single malt scotch whiskies on a five-point scale (0-4) for 12 flavor characteristics: Body, Sweetness, Smoky, Medicinal, Tobacco, Honey, Spicy, Winey, Nutty, Malty, Fruity, Floral. Wishart provides clusterings of the whiskies into 4, 6, and 10 clusters. Young et al. (unpublished manuscript) demonstrate a further clustering into 4 clusters using non-negative matrix factorization (NMF). Both the Young et al. paper and the original data set are available here. (more…)