wehub-resource-sync
30 lines
2.2 KiB
Markdown
30 lines
2.2 KiB
Markdown
# What are some of the issues with Clustering?
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I wouldn't necessarily call most of them "issues" but rather "challenges". For example, *k*-means:
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- The different results via *k*-means with distinct random initializations are definitely a problem. However, we could use *k*-means++ as an alternative, and if it's computationally feasible, we want to run your algorithm multiple times with different seeds and pick the one with e.g., lowest within cluster SSE (sum of squared errors)
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- The number of clusters is (typically) not known a priori (that's basically the characteristic of unsupervised learning problems), but there are a few "performance" or "evaluation metrics one can use to infer a "satisfying" grouping against the value of K; this is also called the elbow method:
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Here, it seems that k=3 would be a good pick. Let's have a look at the accompanying 2D dataset that I used to train the *k*-means algorithm and see if our intuition agrees:
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I'd say k=3 is definitely a reasonable pick. However, note that the "elbow" is typically not as clear as shown above. Moreover, note that in practice we normally work with higher-dimensional datasets so that we can't simply plot our data and double-check visually. (We could use unsupervised dimensionality reduction techniques though such as PCA). In fact, if we already knew that the 3 clusters belong to three different groups, this would be a classification task.
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Anyway, there are other useful evaluation metrics such as the silhouette coefficient, which gives us some idea of the cluster sizes and shapes. Using the same dataset, let me give you a "good" silhouette plot (with k=3) and a not so decent one (k=2)
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I would say that the biggest "shortcoming" in *k*-means may be that we assume that the groups come in spherical or globular shapes, which is rarely the case with "real-world" data. In contrast, I could think of choosing the "optimal" *k* as just another hyperparameter optimization procedure, which is also necessary for almost every supervised learning algorithm.
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