Explore the fundamental concepts and applications of K-means clustering in this 31-minute lecture. Delve into the principles behind this popular unsupervised machine learning algorithm, understanding its methodology for partitioning data into distinct groups. Learn how K-means clustering identifies patterns and similarities within datasets, making it a valuable tool for data analysis and pattern recognition. Discover the algorithm's iterative process, including centroid initialization, assignment of data points, and centroid updates. Examine the algorithm's strengths and limitations, as well as its practical applications in various fields such as market segmentation, image compression, and anomaly detection. Gain insights into optimizing cluster performance and interpreting results to extract meaningful information from complex datasets.
Overview
Syllabus
K - means Clustering
Taught by
NPTEL-NOC IITM
