Types Of Clustering In Machine Learning Training Ppt

Slide 1

This slide lists that there are a variety of Clustering techniques available. The following are the most common clustering approaches used in Machine Learning: Partitioning Clustering, Density-Based Clustering, Distribution Model-Based Clustering, Hierarchical Clustering and Fuzzy Clustering.

Slide 2

This slide illustrates that the data is divided into non-hierarchical groups in Partitioning Clustering or Centroid-Based technique. The K-Means Clustering technique is a well-known example. The dataset is partitioned into K groups, where K denotes the number of pre-defined groups. The cluster center is designed in such a way that the distance between the data points of one cluster and the centroid of another cluster is as little as possible.

Slide 3

This slide states that the density-based clustering approach joins dense areas to form clusters, and arbitrarily shaped distributions are generated as long as the dense region can be linked. The program accomplishes this by detecting distinct clusters in the dataset and connecting high-density areas into clusters.

Instructor Notes: If the dataset has high density and multiple dimensions, these algorithms may struggle to cluster the data points.

Slide 4

This slide explains that the distribution model-based clustering approach divides data based on the chance that a dataset corresponds to a specific distribution. The grouping is accomplished by assuming specific distributions, most notably the Gaussian Distribution.

Instructor Notes: The Expectation-Maximization Clustering method, which employs Gaussian Mixture Models, is an example of this kind (GMM) of clustering.

Slide 5

This slide showcases that as an alternative to partitioned clustering, Hierarchical Clustering can be used as there is no requirement to list the number of clusters to be formed. The dataset is separated into clusters to form a tree-like structure known as a dendrogram.

Slide 6

This slide states that Fuzzy Clustering is a soft technique in which a data object can be assigned to more than one group called clusters. Every dataset has a collection of membership coefficients proportional to the degree of membership of a cluster.