A Fresh View on Clustering
I was never particularly fond of this unsupervised learning methodology that’s under the umbrella of machine learning. It’s not that I didn’t see value in it, but the methods that were available for it when I started delving into it were rudimentary at best and fairly crude. In fact, if I were to do a PhD now, I’d choose a clustering-related topic since there is so much room for improvement that even a simple idea for improving the most popular clustering methods out there is bound to improve them!
However, the fact that data science researchers and machine learning engineers in particular haven’t spent much time looking into clustering doesn’t make clustering a bad methodology. In fact, I’d argue that it’s one of the most insightful ones and it plays an important role in many data science projects, particularly in the data exploration stage.
The key issues with clustering are:
1. The whole set of distance metrics used
2. The fact that the vast majority of clustering methods yield a (slightly) different result every time they are run
3. The need of an external parameter (K) in most clustering methods used in practice, in order to define how many clusters there are
4. The fact that it’s very shallow in its results
There may be more issues with clustering, but these are the most important ones I’ve found. So, if we were to rethink clustering and do it better, we’d need to address each one of these issues. Namely:
1. A new set of distance metrics would be needed, metrics that are not influenced by the dimensional “noise” so much, in the case of many dimensions in the dataset.
2. The option for a deterministic clustering method, one that would optimize the centroid seed before starting the whole clustering process
3. An optimization process would be in place so as to find the best number of clusters. This should include the possibility of a single cluster, in the case where there isn’t enough diversity in the dataset.
4. A multi-level clustering option needs to be available, much like hierarchical clustering but in reverse, i.e. start with the main clusters in the dataset and gradually dig deeper into levels of sub-clusters.
Now, all this may sound simple but it’s not as easy to put into practice. Apart from an in-depth understanding of data science, a quite refined programming ability is needed too, so that the implementation of this clustering approach can be efficient and scalable. Perhaps all this is not even possible with the conventional data analytics framework, but there is not a single doubt in my mind that it is possible in general, and if a high-performance language is used (e.g. Julia), it is even practically feasible.
Naturally, a clustering framework like this one would require a certain level of A.I. to be used. This doesn’t have to be an ANN though, since A.I. can take many forms, not just network-based ones. Whatever the case, conventional statistics-based methods may be largely inadequate, while the very basic machine learning methods for clustering may not be sufficient either.
This illustrates something that many data science practitioners have forgotten: that data science methods evolve, just like other aspects of the craft. New tools may be intriguing, but equally intriguing are the conventional methodological tools, especially if we were to rethink them from a more advanced perspective. This can be beneficial in many ways, such as opening new avenues of data analytics and even synthesizing new data. This, however, is a story for another time...
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.