A Bibliographic View on Constrained Clustering

TL;DR

This paper provides a bibliographic analysis of constrained clustering research, highlighting trends, software availability, and the scarcity of large comparison experiments, with recent focus on applications, deep learning, active learning, and ensemble methods.

Contribution

It offers a comprehensive bibliographic overview and trend analysis of constrained clustering, including software and experimental study insights, which was previously lacking.

Findings

Applications studies are most common recently

Deep learning, active learning, and ensemble learning are prominent topics

There is a notable lack of large comparison experiments

Abstract

A keyword search on constrained clustering on Web-of-Science returned just under 3,000 documents. We ran automatic analyses of those, and compiled our own bibliography of 183 papers which we analysed in more detail based on their topic and experimental study, if any. This paper presents general trends of the area and its sub-topics by Pareto analysis, using citation count and year of publication. We list available software and analyse the experimental sections of our reference collection. We found a notable lack of large comparison experiments. Among the topics we reviewed, applications studies were most abundant recently, alongside deep learning, active learning and ensemble learning.