DBSCAN Algorithm
DBSCAN: Density-based spatial clustering of applications with noise Clustering comes under the Unsupervised learning technique that divides the data points into a number of groups/clusters, such that data points in the same groups have similar features and data points in different groups are dissimilar to each other. In simple words, we can say that the Clustering Algorithm works on feature similarities. Example: Let say we have ten different types of seeds. We can differentiate seeds based on their features like Shape, Color, Size, Test, etc... So, Based on these parameters we will put seeds into different groups. Topics covered in this Article : Types of Clustering Algorithms Introduction to DBSCAN Why DBSCAN? Algorithm steps for DBSCAN Advantage and Disadvantage of DBSCAN Compare DBSCAN with K-Means Types of Clustering Algorithms I. Connectivity based Clustering -> Hierarchical Clustering II. Distribution based Clustering -...