Constrained Clustering: Advances in Algorithms, Theory, and Applications

By Sugato Basu, Ian N. Davidson, Kiri L. Wagstaff. 2008

544 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “rich literature on constraint-based pattern mining [52], [53] and clustering [54].”
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge (2016, positive): “Various types of constraints have also been used extensively to guide unsupervised learning algorithms, such as clustering and dimensionality reduction techniques (Lee and Seung 2001; Basu, Davidson, and Wagstaff 2008; Zhi et al. 2013; Ermon et al. 2015).”
Incorporating Lexical Priors into Topic Models (2012, methodology): “This work is analogous to constrained K -means clustering (Wagstaff et al., 2001; Basu et al., 2008).”
Constrained 1-Spectral Clustering (2012, methodology): “We outperform consistently all other proposed methods in the experiments.”
Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors (2009, background): “Borrowing names from the constrained clustering literature (Basu et al., 2008), we call the two primitives Must-Links and Cannot-Links, although there are important differences.”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, background): “The most common type of information appears in the form of instance-level constraints, which encourage pairs of objects to be grouped together in the same cluster (should-link constraints) or to be separated apart in different clusters (should-not-link constraints) [Wagstaff 2002; Basu et al. 2008].”
Data clustering: 50 years beyond K-means (2008, background): “Constraints can be particularly beneficial in data clustering (Lange et al., 2005; Basu et al., 2008), where precise definitions of underlying clusters are absent.”
Clustering with Multiple Graphs (2009, background): “Here we consider the following two types of instance-level constraints on cluster assignments, which can naturally emerge in various situations [5]: must-link, which constrains entity i and entity j in the same cluster and cannot-link, which constrains entity i and entity j in different clusters.”
On constrained spectral clustering and its applications (2012, background): “How to make use of these side information to improve clustering falls into the area of constrained clustering (Basu et al. 2008).”
The APOGEE-2 Survey of the Orion Star-forming Complex. II. Six-dimensional Structure (2018, background): “In statistical parlance, this is an example of ‘constrained’ or ‘semi-supervised’ clustering (Basu et al. 2008).”
Introduction to Semi-Supervised Learning (2009, background): “Related papers: [15, 16]”
Semi-Supervised Kernel Mean Shift Clustering (2014, background): “Semi-supervised clustering has received a lot of attention in the past few years due to its highly improved performance over traditional unsupervised clustering methods [3], [6].”
Learning Must-Link Constraints for Video Segmentation Based on Spectral Clustering (2014, background): “The usage of must-link constraints, first introduced in [44], is an active area of research in machine learning known as constrained clustering (see [5] for an overview).”