Explaining Black-Box Clustering Pipelines With Cluster-Explorer
Summary: Cluster-Explorer explains black-box clustering by finding concise predicate conjunctions that maximize cluster coverage and minimize overlap. It reduces explanation to generalized frequent-itemset mining with attribute selection/pruning for efficiency and beats XAI baselines on 98 benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Sariel Ofek
- 2. Amit Somech
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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 |
|---|---|---|---|---|
| 33 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00077399244 |
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 182 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036955562 |
| 404 | Mining Generalized Association Rules | 1995 | VLDB | 0.00024159651 |
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