Practical Introduction to Clustering Data
Data Analysis, Statistics and Probability
2016-02-17 v1 Instrumentation and Methods for Astrophysics
Statistical Mechanics
Machine Learning
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.
Keywords
Cite
@article{arxiv.1602.05124,
title = {Practical Introduction to Clustering Data},
author = {Alexander K. Hartmann},
journal= {arXiv preprint arXiv:1602.05124},
year = {2016}
}
Comments
22 pages. All source code in anc directory included. Section 8.5.6 of book: A.K. Hartmann, Big Practical Guide to Computer Simulations, World-Scientifc, Singapore (2015)