BTECH- ELECTRICAL AND ELECTRONICS ENGGI EERING Study Guide - Mahalanobis Distance, Dbscan, Glossary Of Graph Theory Terms

• School: Jawaharlal Nehru Technological University - JNTU Kakinada
• Department: Engineering
• Course: BTECH- ELECTRICAL AND ELECTRONICS ENGGI EERING

Document Summary

Clustering, k-means, limits of k-means, using clustering for image. Segmentation, using clustering for preprocessing, using clustering for. K-means (distance between points), affinity propagation (graph distance), mean- shift (distance between points), dbscan (distance between nearest points), gaussian mixtures (mahalanobis distance to centers), spectral clustering (graph distance) etc. Fundamentally, all clustering methods use the same approach i. e. first we calculate similarities and then we use it to cluster the data points into groups or batches. K-means clustering is an unsupervised learning algorithm, which groups the unlabeled dataset into different clusters. Here k defines the number of pre-defined clusters that need to be created in the process, as if k=2, there will be two clusters, and for k=3, there will be three clusters, and so on. It is an iterative algorithm that divides the unlabeled dataset into k different clusters in such a way that each dataset belongs only one group that has similar properties.