You can find a different clustering with the same dataset at this blog post from Edwin Chen, where also the dataset was originally from. The clusters will consist of products featuring the same nutrition profile. The third dataset is about clustering the McDonalds menu and has more of a “real world character”. You can download them from together with some information about the true number of clusters.

For this we use the datasets named “Aggregation” (1) and “Spiral” (2). This will be done by contrasting the results with those from “classical methods”. Like for most of the data analytics problems, the rule “There is No Free Lunch for the Data Miner” is still valid and hence also the limitations of the approach will be discussed.įor illustration I used three datasets: The first two are artificial datasets and their purpose is to demonstrate the benefits and the limitations from the presented method. I think that this makes it really interesting for a lot of practical problems and time-bounded projects. I will present a method, which tackles the described problem and is also very simple to apply. Because there is no reference when using clustering in an unsupervised fashion, the analyst has to decide whether the results describe some causal or artificial patterns. This makes it hard to decide, which of the results should be kept. The difficulty is the following: Every clustering algorithm and even any set of parameters will produce a somewhat different solution. The challenge here is the “freedom of choice” over a broad range of different cluster algorithms and how to determine the right parameter values. Today’s blog post is about a problem known by most of the people using cluster algorithms on datasets without given true labels (unsupervised learning). The Wisdom of Crowds - Clustering Using Evidence Accumulation Clustering (EAC)

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