Fully Explained OPTICS Clustering with Python Example

As we know that Clustering is a powerful unsupervised knowledge discovery tool used nowadays to segment our data points into groups of similar features types. However, each algorithm of clustering works according to the parameters. Similarity-based techniques (K-means clustering algorithm working is based on similarity of the data points and is tasked with designating how many clusters are available, while hierarchical clustering algorithms decide when to assign finished clusters manually. Generally used density-based clustering technique is DBSCAN which requires two parameters about how it defines its Core Points, but finding the parameters is an extremely difficult task. DBSCAN's relatively algorithm is called OPTICS (Ordering Points to Identify Cluster Structure).