Clustering 1

This study is to practice the Unsupervised Learning section (Chapter 7) of Practical Statistics for Data Scientists book. We'll give some information about $k$-Means Clustering, Hierarchical Clustering and Model-Based Clustering using Python on Jupyter Notebook.

Clustering methods can be used to identify meaningful groups of data. For example, using the web clicks and demographic data of a user on a website, we may be able to group together different types of users ².

$k$-Means Clustering is an algorithm that groups data points into 'K' distinct clusters based on their similarity in order to discover patterns or structure in the data. In this method, the goal is to partition a dataset into 'K' clusters, where 'K' is a predefined number specified by the user. The algorithm iteratively assigns each data point to the cluster with the closest centroid (mean) and updates the centroids based on the new assignments.

Hierarchical clustering is an alternative to $k$-Means that allows the user to visualize the effect of specifying different numbers of clusters. It is more sensitive in discovering outlying or aberrant groups or records. Hierarchical clustering’s flexibility comes with a cost, and hierarchical clustering does not scale well to large data sets with millions of records ².

Model-based clustering is a technique that assigns data points to clusters based on probabilistic models. It assumes that the data is generated from a mixture of distributions, and uses statistical inference to estimate the model parameters and assign data points to clusters based on the likelihood of belonging to each distribution.