July 2017
Beginner to intermediate
715 pages
17h 3m
English
Content preview from Java: Data Science Made Easy
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DBSCAN
DBSCAN is another clustering quite popular technique. DBSCAN belongs to the density-based family of algorithms, and, unlike K-Means, it does not need to know the number of clusters, K, in advance.
In a few words, DBSCAN works as follows: at each step it takes an item to grow a cluster around it.
When we take an item from a high-density region, then there are many other data points close to the current item, and all these items are added to the cluster. Then the process is repeated for each newly added element of the cluster. If, however, the region is not dense enough, and there are not so many points nearby, then we do not form a cluster and say that this item is an outlier.
So, for DBSCAN to work, we need to provide the following ...
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