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His analysis of the data led to the question: where did the source data come from in the first place? With some crowdsourced sleuthing, Christopher discovered the data comes from the first edition of the book Whisky Classified: Choosing Single Malts by Flavour by David Wishart. The story behind the data is quite interesting, and worth checking out if you're a whisky fan.

It turns out the data file Luba used comes from the first edition of the "Whisky Classified" book, and there were a few typos in the data to boot (for example, Bowmore had a Medicinal ranking of 1, but was actually a 2 in the book.) A commenter "Florin" at the Scotch and Ice Cream blog cleaned up the data and re-ran the analysis, and generated four slightly different clusters: peaty whiskies, ex-sherry whiskies, ex-bourbon / no peat whiskies, and whiskies with some ex-sherry blended in or with some peat. Extending the analysis to five clusters apparently succeeded in "separating the hard-core peated whiskies from the less-peated ones".

Just goes to show: with just 86 rows of data here, you don't always need "Big Data" to generate interesting analysis!

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