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How can we use unsupervised learning techniques on a data-set, and then label the clusters?

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  • 181
    First up, this is most certainly homework (so no full code samples please). That said...
    I need to test an unsupervised algorithm next to a supervised algorithm, using the Neural Network toolbox in Matlab. The data set is the UCI Artificial Characters Database. The problem is, I've had a good tutorial on supervised algorithms, and been left to sink on unsupervised.
    So I know how to create a self organising map using selforgmap, and then I train it using train(net, trainingSet). I don't understand what to do next. I know that it's clustered the data I gave it into (hopefully) 10 clusters (one for each letter).
    Two questions then:
    • How can I then label the clusters (given that I have a comparison pattern)?
      • Am I trying to turn this into a supervised learning problem when I do this?
    • How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?
    I think I'm missing something conceptual or jargon-based here - all my searches come up with supervised learning techniques. A point in the right direction would be much appreciated. My existing code is below:
    P = load('-ascii', 'pattern'); T = load('-ascii', 'target'); % data needs to be translated P = P'; T = T'; T = T(find(sum(T')), :); mynet = selforgmap([10 10]); mynet.trainparam.epochs = 5000; mynet = train(mynet, P); P = load('-ascii', 'testpattern'); T = load('-ascii', 'testtarget'); P = P'; T = T'; T = T(find(sum(T')), :); Y = sim(mynet,P); Z = compet(Y); % this gives me a confusion matrix for supervised techniques: C = T*Z'
      September 18, 2021 6:15 PM IST
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  • 126
    Since you don't employ any part of labelled data you are applying an unsupervised method by definition.

    "How can I then label the clusters (given that I have a comparison pattern)?"

    You can try different perturbations of the label-set and keep the one the minimizes the average error (or accuracy) on the comparison pattern. With clustering, you can label your clusters in any way you like. Think of it like trying different label assignments until you minimizes a specified performance metric.

    "Am I trying to turn this into a supervised learning problem when I do this?"

    It depends. If you explicitly use (known) data-points in the process of clustering, then this is semi-supervised. If not, you merely use the labeling information to evaluate and "compare" with supervised approaches. It is a form of supervision, but not based on training set, but on the best-case expected performance (i.e. an "agent" specifies correct labels to clusters).

    "How can I create a confusion matrix on (another) testing set to compare to the supervised algorithm?"

    You need a way to turn clusters into labelled classes. For a small number of clusters (e.g. C <= 5), you could essentially create C! matrices, and keep the one that minimizes your average classification error. In your case however, with C = 10, this is, obviously, impractical and a grave overhead!
    As alternatives, you can label the clusters (and thus obtain confusion matrices) using:
    • Semi-supervised approaches, where the clusters may be labelled a-priori, or guided through a seeding process by data belonging to known cluster/classes.
    • Ranking or finding distances between the estimated cluster centroids and the ground-truth labels. This will assign the closest-ranked or most similar label to each cluster.
      September 21, 2021 6:43 PM IST
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  • 84

    Could this video be of any help? It doesn't answer your question but it shows that human interaction may be required to even select number of clusters. Automatically labeling clusters is even harder.

    If you think about it there's no guarantee that clustering will be done based on the depicted number. Network might group digits based on width of the line or on the smoothing of the font, etc.

      September 21, 2021 11:48 PM IST
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  • 259
    Hello. Unsupervised clustering methods create groups with instances that have similarities. If you do not have the classes associated with data set, you can use clustering methods for finding out related instances. An especialist can verify and define labels (classes) for groups.


    You can use your clustering method on data with labels removed and then check its efficiency by counting how many samples labeled with a similar class went to the same clusters. The trick here is that you cannot use precision, recall etc. metrics that you usually use to check the efficiency of classification. The most common metrics for clustering evaluation are Rand Jaccard, B-cubed. Here in paragraph "5.3 Evaluating clusters" I suggest to use F-measure:
    You can see in the formula how different it is from the F-measure used for analysis of classification. It is important that you will not be able to compare efficiency of your clustering method to classification ones.
    But if by classification you don't mean a Machine Learning method, but just that you want to use your clusters as a basis for terminological research - for example, that these clusters relate to some expert-defined classes of disorders, then you need to compare your own clustering method to other clustering (sic!) methods, not ML classification ones.
      September 22, 2021 2:17 PM IST
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