Practical Introduction to Clustering Data

TL;DR

This paper provides a practical introduction to data clustering, covering three basic algorithms with implementation examples to help readers understand and apply clustering techniques.

Contribution

It introduces three fundamental clustering methods—k-means, neighbor-based, and agglomerative—with accompanying C code for easy implementation.

Findings

Provides clear explanations of clustering algorithms

Includes practical C code examples

Facilitates understanding and application of clustering methods

Abstract

Data clustering is an approach to seek for structure in sets of complex data, i.e., sets of "objects". The main objective is to identify groups of objects which are similar to each other, e.g., for classification. Here, an introduction to clustering is given and three basic approaches are introduced: the k-means algorithm, neighbour-based clustering, and an agglomerative clustering method. For all cases, C source code examples are given, allowing for an easy implementation.