Machine Learning: All About OPTICS Clustering & Implementation in Python
All Machine learning enthusiasts would know basic clustering algorithms like k-means etc. In this post I am going to talk about OPTICS Clustering and the implementation of Optics Clustering in Python. Moreover I would also talk about this clustering algorithm features alongside with its drawbacks.
I would start with the basics just for a refresher.
What is a clustering algorithm ?
Cluster analysis, or clustering, is an unsupervised machine learning task.
It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space. Clustering techniques apply when there is no class to be predicted but rather when the instances are to be divided into natural groups.
Introduction to OPTICS Clustering?
What is OPTICS clustering? Density-based clustering algorithms aim to achieve the same thing as k-means and hierarchical clustering: partitioning a dataset into a finite set of clusters that reveals a grouping structure in our data. and this Ordering points to identify the clustering structure (OPTICS) is one of the density based clustering.
