Exploring Clustering Algorithms: How to Master the Art of Data Grouping with Python
What is Clustering? Clustering is a type of Unsupervised Learning . it refers to a set of techniques for finding subgroups or clusters (collections of data based on similarity) in a dataset. Clustering Algorithms Clustering techniques are used for investigating data, identifying anomalies, locating outliers, or seeing patterns in the data. There different types of clustering Algorithms in machine learning,these include; K-Means clustering Mini batch K-Means clustering algorithm Hierarchical Agglomerative clustering. density-based clustering algorithm (DBSCAN) In this blog post, I would like to explore K-Means clustering Algorithms, how it works, and how to implement it with Python and Scikit-learn. K-Means Clustering Algorithms In K-Means, Centroids are calculated via the K-means clustering algorithm, which then iterates until the best centroid is discovered. How K-Means Clustering Algorithms Work? K-Means steps Implementation Import libraries import random import numpy as np impor
