Research map / Learning theory and algorithms / Matrix and tensor methods
Row-clustering of a Point Process-valued Matrix
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MS-MPP instead of DF for single-level marked point-process clustering | Synthetic clustering, m=1 · Synthetic, m=1, C=2–5 | clustering purity: 0.597 -> 0.831; 0.514 -> 0.767; 0.443 -> 0.714; 0.379 -> 0.675 | Helped |
| Use MM-MPP instead of DF for repeated-event clustering | Synthetic clustering, m=20 · Synthetic, m=20, C=2–5 | clustering purity: 0.536 -> 0.947; 0.465 -> 0.902; 0.422 -> 0.874; 0.351 -> 0.835 | Helped |
| Use MM-MPP instead of DF for repeated-event clustering | Synthetic clustering, m=100 · Synthetic, m=100, C=2–5 | clustering purity: 0.532 -> 0.988; 0.477 -> 0.967; 0.436 -> 0.944; 0.333 -> 0.919 | Helped |
| Use MM-MPP instead of DMHP on real-world datasets | Real-world row clustering · Twitter | clustering consistency: 0.275 -> 0.394 | Helped |
| Use MM-MPP instead of DMHP on real-world datasets | Real-world row clustering · Credit Card | clustering consistency: 0.331 -> 0.378 | Helped |
| Use MM-MPP instead of DMHP on real-world datasets | Real-world row clustering · Chicago Taxi | clustering consistency: 0.142 -> 0.153 | Helped |
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