K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center . This article explores k-means clustering, its importance, applications, and workings, providing a clear understanding of its role in data analysis. This course focuses on k-means because it scales as O (n k), where k is the number of clusters chosen by the user. This algorithm groups points into k clusters by minimizing the... K-Means clustering can be used to analyze gene expression data to identify different groups of genes that are co-regulated or co-expressed. This technique is widely used in bioinformatics applications, such as drug discovery, disease diagnosis, and personalized medicine. Applications kmeans algorithm is very popular and used in a variety of applications such as market segmentation, document clustering , image segmentation and image compression, etc. The goal usually when we undergo a cluster analysis is either: Get a meaningful intuition of the structure of the data we’re dealing with. Cluster -then-predict where different models will be built for different subgroups if we believe there is a wide variation in the behaviors of different subgroups. An example ...
