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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)

Paper ID
9789
Venue
VLDB
Year
2007
Pagerank
5.4620216e-05
Overall Rank
8,229 | 43.55%
DOI
-

Incoming Non-self Citations Over Time

Authors

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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