I have a Bachelors degree and doing MBA degree in Buisness Analytics and I’ve worked on machine learning systems for startups, and severe forecasting.
I started this community for two main reasons:
1) Because I find machine learning endlessly fascinating.
2) Because I want to help developers get started and get good at applied machine learning.
I see a lot of developers not getting started, “getting ready” to get started, and generally studying the wrong things, and I think it is a huge waste of time.
I created this site to show developers another way.
Please connect with me or follow me on: Linkedin
DBSCAN is a short-form of D ensity- B ased S patial C lustering of A pplications with N oise. It is an unsupervised algorithm that will take the set of points and make them into some sets which have the same properties. It is based on the density-based clustering and it will mark the outliers also which do not lie in any of the cluster or set. There are some terms that we need to know before we proceed further for algorithm: Density Reachability A point “p” is said to be density reachable from a point “q” if point “p” is within ε distance from point “q” and “q” has a sufficient number of points in its neighbors which are within distance ε. Density Connectivity A point “p” and “q” are said to be density connected if there exists a point “r” which has a sufficient number of points in its neighbors and both the points “p” and “q” is within the ε distance. This is a chaining process. So, if “q” is neighbor of “r”, “r” is neighbor of “s”, “s” ...

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