Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data
Summary: Proposes a time-series data cube for multi-dimensional market segments to capture subspaces and detect anomalies via higher-level expectations. Introduces an efficient iterative subspace search to identify approximate top-k anomalies in each subspace; validated on synthetic and real-world data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaolei Li (University of Illinois Urbana-Champaign)
- 2. Jiawei Han (University of Illinois Urbana-Champaign)
BibTeX Citation
@article{li_vldb07,
title = {{Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data}},
author = {Li, Xiaolei and Han, Jiawei},
journal = {PVLDB},
series = {{VLDB} '07},
pages = {447--458},
year = {2007}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,414 | GAMPS: Compressing Multi Sensor Data by Grouping and Amplitude Scaling | 2009 | SIGMOD | 5.624223e-05 |
| 11,418 | Efficient Approximation Framework for Attribute Recommendation | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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