BIRCH clustering algorithm is provided as an alternative to MinibatchKMeans. It converts data to a tree data structure with the centroids being read off the leaf. And these centroids can be the final cluster centroid or the input for other cluster algorithms like AgglomerativeClustering.
A very common example of an unsupervised machine learning, clustering is the process of grouping similar data points into a cluster. Given a finite set of data points, clustering aims to find homogeneous subgroups of data points with similar characteristics. In this article, we will learn the basics of a simple clustering algorithm called K-Means […]
Cluster analysis is the statistical method of grouping data into subsets that have application in the context of a selective problem. This technique is widely used to club data/observations in the right segments so that data within any segment are similar while data across segments are different. However, defining “similar” or “different” observations is a […]
Unsupervised learning has emerged as the most effective technique for discovering patterns in data. It is also being used to develop labels on top of the supervised models. This is one of the most widely used techniques for market or customer segmentation wherein the company’s data can be segregated into clusters and used to identify […]