Clustering with Instance-Level Constraints

By Kiri L. Wagstaff, Claire Cardie. AAAI/IAAI, 2000

688 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/17ad99229140cf72e4495412c44c73d52cc9d913

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Citations in the corpus

Distance Metric Learning with Application to Clustering with Side-Information (2002, methodology): “Following [12], in the case of 2-cluster data, we will measure how well the ĉi’s match the ci’s according to”
Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge (2015, background): “For instance, using either class labels [27] (indicating exact categories) or pairwise constraints (indicating whether two samples belong to the same class) [40] as prior information is acceptable.”
Clustering with Constraints: Feasibility Issues and the k-Means Algorithm (2005, methodology): “We can divide previous work on clustering under constraints into two types: 1) Where the constraints help the algorithm learn a distortion/distance/objective function [4, 15] and 2) Where the constraints are used as “hints” to guide the algorithm to a useful solution [18, 19].”
Active co-analysis of a set of shapes (2012, methodology): “These constraints have been incorporated into many clustering algorithms, such as K-means clustering [Wagstaff and Cardie 2000], hierarchical clustering [Klein et al. 2002], and clustering with Gaussian mixture models [Shental et al. 2004].”
Semi-supervised Kernel Metric Learning Using Relative Comparisons (2016, methodology): “The idea of semi-supervised clustering was initially introduced by Wagstaff and Cardie [2000], and since then a large number of different problem variants and methods have been proposed, the ﬁrst ones being COP-Kmeans [Wagstaff et al., 2001] and CCL [Klein et al., 2002].”
Correlation clustering in general weighted graphs (2006, background): “There has been a great deal of work on solving clustering problems subject to constraints [18,29,30,15].”
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering (2002, methodology): “Following Wagstaff and Cardie (2000), we use a modification of the Rand index, suitable for constrained clustering.”