The Gibbs–Rand Model
Summary: Proposes the first Gibbs-like generative model for clustering ensembles: probability ∝ exp(−scale · scaled Rand distance) around a center clustering. Provides poly-time sampling for constant-k centers and reconstruction for small scale, revealing richer combinatorics than the Mallows model. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Flavio Chierichetti
- 2. Ravi Kumar
- 3. Silvio Lattanzi
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,810 | A Model-based Approach to Attributed Graph Clustering | 2012 | SIGMOD | 8.085151e-05 |
| 3,296 | RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning | 2018 | SIGMOD | 7.2553154e-05 |
| 8,534 | Biclustering and Boolean Matrix Factorization in Data Streams | 2020 | VLDB | 4.4893996e-05 |
| 8,537 | A Query Engine for Probabilistic Preferences | 2018 | SIGMOD | 4.4893996e-05 |
| 8,538 | On Asymptotic Cost of Triangle Listing in Random Graphs | 2017 | PODS | 4.4893996e-05 |
| 8,539 | Querying Probabilistic Preferences in Databases | 2017 | PODS | 4.4893996e-05 |
| 8,544 | Advancing Data Clustering via Projective Clustering Ensembles | 2011 | SIGMOD | 4.4893996e-05 |
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