Clustering Algorithms In Machine Learning Training Ppt

Slide 1

This slide lists the different types of clustering algorithms in unsupervised Machine Learning. These include K-Means, mean-shift, DBSCAN, expectation-maximization clustering using GMM, agglomerative hierarchical algorithm, and affinity propagation.

Slide 2

This slide gives an overview of K-Means clustering algorithm which is an unsupervised approach in which the samples are divided into separate clusters with equal variances to classify the data.

Slide 3

This slide introduces the mean-shift algorithm, which attempts to locate dense areas within a smooth distribution of data points. It's an example of a centroid-based model that updates candidates for centroid to be the center of points within a specified region.

Slide 4

This slide states that DBSCAN algorithm stands for Density-Based Spatial Clustering of Applications with Noise. It's a density-based model comparable to the mean-shift model but with a few notable improvements. The high-density zones are distinguished from the low-density areas using this approach.

Slide 5

This slide showcases that Expectation-Maximization Clustering using GMM algorithm can be used as a replacement for the k-means algorithm or in situations when the k-means algorithm fails. The data points in GMM are supposed to be Gaussian distributed.

Slide 6

This slide lists that Agglomerative hierarchical algorithm carries out the bottom-up hierarchical clustering. Each data point is initially regarded as a single cluster and then gradually merged in this method.

Slide 7

This slide gives an overview of affinity propagation which is different from other clustering methods since it does not require the number of clusters to be specified. Each data point delivers a message between the pair of data points until convergence.