DBScan Clustering Algorithm

#artificialintelligence 

Clustering is an important topic in busyness, because it helps us to reduce the number of features to some typology, to some clusters which, in a case that data allows us, can give us more informations about our topic of interest. In a data science literature it is usually presented as dimension reduction technique, but in science, or even in data science it could reveal some additional pattern in data that is not obvious at the first glance. Imagine you have some features about some students: their marks, their personality traits, their ability scores, their motivation. Clustering could reveal you the completely new types of (un)successful students (it could be someone with high ability and low motivation -- underachiever, but at the same time it could be someone with high motivation and really good marks, but low abilities -- overachiever). This could simply done by clustering, while our cluster names (overachiever, underachiever) are basically interpretations of the clusters.

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