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The Bayesian Sorting Hat: A Decision-Theoretic Approach to Size-Constrained Clustering
Applications · Submitted 17 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim As opposed to prior work, our approach is uniquely suited to situations in which size constraints reflect and external limitation or desire rather than an internal feature of the data generation process.
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The abstract, sentence by sentence
Size-constrained clustering (SCC) refers to the dual problem of using observations to determine latent cluster structure while at the same time assigning observations to the unknown clusters subject to an analyst defined constraint on cluster sizes. While several approaches have been proposed, SCC remains a difficult problem due to the combinatorial dependency between observations introduced by the size-constraints. Here we reformulate SCC as a decision problem and introduce a novel loss function to capture various types of size constraints. As opposed to prior work, our approach is uniquely suited to situations in which size constraints reflect and external limitation or desire rather than an internal feature of the data generation process. To demonstrate our approach, we develop a Bayesian mixture model for clustering respondents using both simulated and real categorical survey data. Our motivation for the development of this decision theoretic approach to SCC was to determine optimal team assignments for a Harry Potter themed scavenger hunt based on categorical survey data from participants.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Size-constrained clustering (SCC) refers to the dual problem of using observations to determine latent cluster structure while at the same time assigning observations to the unknown clusters subject to an analyst defined constraint on cluster sizes. | background 1.00 | 0.06 | 0.04 | asserted 0.99 |
| s1 | While several approaches have been proposed, SCC remains a difficult problem due to the combinatorial dependency between observations introduced by the size-constraints. | background 0.62 | 0.05 | 0.03 | asserted 1.00 |
| s2 | Here we reformulate SCC as a decision problem and introduce a novel loss function to capture various types of size constraints. | contribution 0.91 | 0.95 | 0.16 | asserted 1.00 |
| s3 | As opposed to prior work, our approach is uniquely suited to situations in which size constraints reflect and external limitation or desire rather than an internal feature of the data generation process. | contribution 0.91 | 0.50 | 0.14 | asserted 1.00 |
| s4 | To demonstrate our approach, we develop a Bayesian mixture model for clustering respondents using both simulated and real categorical survey data. | method 0.79 | 0.94 | 0.07 | asserted 1.00 |
| s5 | Our motivation for the development of this decision theoretic approach to SCC was to determine optimal team assignments for a Harry Potter themed scavenger hunt based on categorical survey data from participants. | background 0.66 | 0.84 | 0.04 | asserted 1.00 |
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Original abstract
Size-constrained clustering (SCC) refers to the dual problem of using observations to determine latent cluster structure while at the same time assigning observations to the unknown clusters subject to an analyst defined constraint on cluster sizes. While several approaches have been proposed, SCC remains a difficult problem due to the combinatorial dependency between observations introduced by the size-constraints. Here we reformulate SCC as a decision problem and introduce a novel loss function to capture various types of size constraints. As opposed to prior work, our approach is uniquely suited to situations in which size constraints reflect and external limitation or desire rather than an internal feature of the data generation process. To demonstrate our approach, we develop a Bayesian mixture model for clustering respondents using both simulated and real categorical survey data. Our motivation for the development of this decision theoretic approach to SCC was to determine optimal team assignments for a Harry Potter themed scavenger hunt based on categorical survey data from participants.
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Previous work includes models that sacrifice finite exchangeability to handle data with a temporal component [ref] , general finite mixture models with constraints on cluster sizes [ref] , and models for sparse networks based on random partitions with power-law distributed cluster sizes [ref] .
Fast Generation of Exchangeable Sequence of Clusters Data · Related work on ESC models · 1.00
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