Welcome to Cluster Analysis in Data Mining! The problem of clustering is to take a collection of points and group them into "clusters" such that members of the same cluster are close, while members of different clusters are far.

Clustering is an Unsupervised Learning subject and is very crucial for various applications like Categorization, Data preprocessing, Data Visualization, Outlier mining and many more. In this course we explore the different techniques used for clustering data. Cluster analysis itself is not one specific algorithm, but the general task that is to be solved. It can be achieved in different ways that differ significantly in their understanding of a Cluster and how to efficiently find those clusters. In general terms, clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can thus be formulated as a multi-objective Optimization problem.

In this course we will study the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods, and density-based methods such as DBSCAN. We will also learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Course Contents:

- - - - - - - - - - - -

Clustering Algorithms

K-Means Clustering

Density-based clustering - DBSCAN

Hierarchical Clustering

Similarity Measures

Clustering Validation Methods

Good luck as you get started, and I hope you enjoy the course!