A comprehensive guide to 10 clustering algorithms commonly used for Hierarchical, Partitional, and Density-Based Clustering
Clustering algorithms play an important role in data analysis. These unsupervised learning, exploratory data analysis tools provide systems for knowledge discovery by categorizing data points into distinct groups based on shared characteristics. This allows for the identification of relationships and trends that may be hard to see in the raw data. They facilitate more informed decision making by systematically adding more understanding to complex and intricate datasets.
In this article, we will cover the basics of three types of clustering algorithms: Hierarchical, Partitional, and Density-Based Clustering models. We will begin by defining each of these categories. Next, we will dive into 10 different clustering algorithms, providing definitions, links to the original or interesting research papers, strengths of the algorithms, and python code-snippets for each.
Table of Contents
Hierarchical Clustering Algorithms
Partitional Clustering Algorithms
Density-Based Clustering Algorithms
Definition: Hierarchical clustering is a method of cluster analysis that builds a hierarchy of clusters. It can be visualized as a tree structure (dendrogram) where the leaves represent individual data points and the root represents a single cluster containing all data points.
Use Cases:
Taxonomy Problems.When Vertical relationships are important in the data.
Strengths:
Provides a hierarchical structure of clusters.
