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A Bibliographic View on Constrained Clustering

arXiv 2209.11125 ↗First posted 22 Sep 2022cs.LG cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 186 references that are in the map. First posted on arXiv 22 Sep 2022.

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14 of 186 references.

References · 14

  1. 2019Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinementcited by 56
  2. 2021Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrievalcited by 27
  3. 2019A Framework for Deep Constrained Clustering -- Algorithms and Advancescited by 24
  4. 2015Neural network-based clustering using pairwise constraintscited by 22
  5. 2013Local algorithms for interactive clusteringcited by 14
  6. 2020Interactive Steering of Hierarchical Clusteringcited by 14
  7. 2020A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraintscited by 10
  8. 2019What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018cited by 9
  9. 2018Semi-Supervised Clustering with Neural Networkscited by 7
  10. 2020A semi-supervised sparse K-Means algorithmcited by 3
  11. 2015Constrained 1-Spectral Clusteringcited by 2
  12. 2015Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithmcited by 2
2 more
  1. 2016Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitationcited by 1
  2. 2022Multi-objective Semi-supervised Clustering for Finding Predictive Clusterscited by 1

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