Module 14 Spotlight on Clustering
by Patrick Boily and Jen Schellinck, with contributions from Aditya Maheshwari
In Machine Learning 101, we provided a (basically) math-free general overview of machine learning.
Supervised learning methods can be presented in a formalism which generalizes statistical and regression analysis, and their performance are easy to evaluate; consequently, they have been studied extensively and often form the backbone of machine learning training.
On the other hand, apart from a select few classical models, unsupervised learning tasks are not usually presented with quite the same depth, primarily due to the vagueness which infect their core – a number of the important concepts are ambiguously defined; the validation of the results is often elusive, and the actionable applications of the outcomes are not usually clear.
The interest in such methods and tasks (clustering and segmentation, association rules mining, link profiling, etc.) is mounting, however, with the increased interest in artificial intelligence and machine learning research. In this module, we describe various clustering algorithms, and discuss related issues and challenges.
14.4 Advanced Clustering Methods
14.4.1 Density-Based Clustering
14.4.2 Spectral Clustering
14.4.3 Probability-Based Clustering
14.4.4 Affinity Propagation
14.4.5 Fuzzy Clustering
14.4.6 Cluster Ensembles