OPTICS is an acronym that stands for Ordering Points to Identify the Clustering Structure. It is a density-based method similar to DBSCAN, but it is superior in that it can locate meaningful clusters in data with varying densities. It accomplishes this by arranging the data points in such a way that the nearest points are neighbors in the arrangement. This makes detecting various density clusters easier. Similarly to DBSCAN, the OPTICS algorithm only processes each data point once (although it runs slower than DBSCAN). Each data point also has a particular distance saved that indicates whether or not it belongs to a given cluster.


  • from sklearn.cluster import OPTICS

Full Code Of Implementing OPTICS Clustering Algorithm

We will practice about the OPTICS algorithm (Download the dataset here) which is about grouping consumers using an unsupervised learning approach called OPTICS (ordering points to identify the clustering structure). In other words, we find clusters based on density, which is defined as groups of data points that are excessively packed with data. In our application, we will recognize consumers that are sufficiently similar to one another; these client segments should be dense enough to be regarded a single cluster.