Efficient Algorithm for K-Multiple-Means
Summary: F-KMM speeds up K-Multiple-Means by computing leading singular vectors from a compact mean–mean similarity matrix, capturing non-spherical clusters. It preserves exact results while skipping unnecessary distance computations with lower-bound estimates, delivering orders-of-magnitude speedups on large data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yasuhiro Fujiwara
- 2. Atsutoshi Kumagai
- 3. Yasutoshi Ida
- 4. Masahiro Nakano
- 5. Makoto Nakatsuji
- 6. Akisato Kimura
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 159 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00040135453 |
| 11,963 | Scaling Manifold Ranking Based Image Retrieval | 2015 | VLDB | 4.1905499e-05 |
| 13,350 | Fast Algorithm for the Lasso based L1-Graph Construction | 2017 | VLDB | - |
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