CODEWITHSUNDEEP
cluster-analysisk-meansunsupervised-learning
james.bondu
james.bondu

Reputation: 1158

What is the importance of clustering?

During unsupervised learning we do cluster analysis (like K-Means) to bin the data to a number of clusters.
But what is the use of these clustered data in practical scenario.

I think during clustering we are losing information about the data.
Are there some practical examples where clustering could be beneficial?

Upvotes: -2

Views: 1278

Answers (1)

Has QUIT--Anony-Mousse
Has QUIT--Anony-Mousse

Reputation: 77485

The information loss can be intentional. Here are three examples:

  • PCM signal quantification (Lloyd's k-means publication). You know that are certain number (say 10) different signals are transmitted, but with distortion. Quantifying removes the distortions and re-extracts the original 10 different signals. Here, you lose the error and keep the signal.
  • Color quantization (see Wikipedia). To reduce the number of colors in an image, a quite nice method uses k-means (usually in HSV or Lab space). k is the number of desired output colors. Information loss here is intentional, to better compress the image. k-means attempts to find the least-squared-error approximation of the image with just k colors.
  • When searching motifs in time series, you can also use quantization such as k-means to transform your data into a symbolic representation. The bag-of-visual-words approach that was the state of the art for image recognition prior to deep learning also used this.
  • Explorative data mining (clustering - one may argue that above use cases are not data mining / clustering; but quantization). If you have a data set of a million points, which points are you going to investigate? clustering methods try ro split the data into groups that are supposed to be more homogeneous within and more different to another. Thrn you don't have to look at every object, but only at some of each cluster to hopefully learn something about the whole cluster (and your whole data set). Centroid methods such as k-means even can proviee a "prototype" for each cluster, albeit it is a good idea to also lool at other points within the cluster. You may also want to do outlier detection and look at some of the unusual objects. This scenario is somewhere inbetween of sampling representative objects and reducing the data set size to become more manageable. The key difference to above points is that the result is usually not "operationalized" automatically, but because explorative clustering results are too unreliable (and thus require many iterations) need to be analyzed manually.

Upvotes: 1

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