In the context of a semi-supervised learning problem, what's the difference between using a classification algorithm vs a clustering algorithm?
Traditionally classification is supervised and clustering is unsupervised. However in this context, unlike traditional supervised learning, only a very small amount of labeled data is used. This blurs the lines between the two.
Both modified classification algorithms and modified clustering algorithms exist to solve semi-supervised problems. However, in every scenario I can think to try, the majority of modified (or even sometime unmodified) classification algorithms significantly out perform the modified clustering algorithms.
If this is the case, why is there so much interest in creating modified clustering algorithms? (based on the multitude of papers attempting this)