Clustering Concepts: Difference between revisions
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=Overview= | =Overview= | ||
When we talk about "clustering" when we have a set of n "points", which we may think about as points in space in geometrical sense. It is actually quite rare that the underlying problem we care about is intrinsically geometric. Usually we are representing something else we care about (web pages, genome sequence fragments, etc.) and we want to cluster them in coherent groups. In machine learning, the same problem is referred to as "unsupervised learning", meaning that the data is unlabeled and we are looking for patterns in data, when data is not annotated. | |||
=The Clustering Problem= | =The Clustering Problem= | ||
Revision as of 20:21, 23 October 2021
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Overview
When we talk about "clustering" when we have a set of n "points", which we may think about as points in space in geometrical sense. It is actually quite rare that the underlying problem we care about is intrinsically geometric. Usually we are representing something else we care about (web pages, genome sequence fragments, etc.) and we want to cluster them in coherent groups. In machine learning, the same problem is referred to as "unsupervised learning", meaning that the data is unlabeled and we are looking for patterns in data, when data is not annotated.