February 2019
Intermediate to advanced
386 pages
9h 54m
English
Content preview from Hands-On Unsupervised Learning with Python
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Soft Clustering and Gaussian Mixture Models
In this chapter, we will discuss the concept of soft clustering, which allows us to obtain a membership degree for each sample of a dataset with respect to a defined cluster configuration. That is, considering a range from 0% to 100%, we want to know to what extent xi belong to a cluster. The extreme values are 0, which means that xi is completely outside the domain of the cluster and 1 (100%), indicating that xi is fully assigned to a single cluster. All intermediate values imply a partial domain of two or more different clusters. Therefore, in contrast with hard clustering, here, we are interested in determining not a fixed assignment, but a vector with the same properties of a probability distribution ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.