StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams
Summary: StreamMiner is a random-decision-tree ensemble engine for mining concept-drifting streams. It detects drift online without labels and systematically selects historical data and new chunks to retrain an adaptive model. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Wei Fan (International Business Machines Thomas J. Watson Research)
BibTeX Citation
@article{fan_vldb04,
title = {{StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams}},
author = {Fan, Wei},
journal = {PVLDB},
series = {{VLDB} '04},
pages = {1257--1268},
doi = {10.1016/B978-012088469-8.50121-2},
url = {https://doi.org/10.1016/B978-012088469-8.50121-2},
year = {2004}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 26 | Models and Issues in Data Stream Systems | 2002 | PODS | 0.00052121228 |
| 83 | Space-Efficient Online Computation of Quantile Summaries | 2001 | SIGMOD | 0.00035978046 |
| 2,208 | Multi-Dimensional Regression Analysis of Time-Series Data Streams | 2002 | VLDB | 8.8382452e-05 |
| 4,003 | Continually Evaluating Similarity-Based Pattern Queries on a Streaming Time Series | 2002 | SIGMOD | 6.8604652e-05 |
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