Filtering and refinement: a two-stage approach for efficient and effective anomaly detection
Yu, Xiao
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Yu_Xiao.pdf
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https://hdl.handle.net/2142/24511
Description
Title
Filtering and refinement: a two-stage approach for efficient and effective anomaly detection
Author(s)
Yu, Xiao
Issue Date
2011-05-25T14:25:58Z
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2011-05-25T14:25:58Z
Keyword(s)
anomaly detection
Abstract
Anomaly detection is an important data mining task. Most existing methods treat anomalies as inconsistencies and spend the majority amount of time on modeling normal instances. A recently proposed, sampling-based approach may substantially boost the efficiency in anomaly detection but may lead to weaker accuracy and robustness. In this study, we propose a two-stage approach to find anomalies in complex datasets with high accuracy as well as low time complexity and space cost. Instead of analyzing normal instances, our algorithm first employs an efficient deterministic space partition algorithm to eliminate obvious normal instances and generates a small set of anomaly candidates with a single scan of the dataset. It then checks each candidate with density-based multiple criteria to determine the final results. This two-stage framework also detects anomalies of different notions. Our experiments show that this new approach finds anomalies successfully in different conditions and ensures a good balance of efficiency, accuracy, and robustness.
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Yu_Xiao.pdf
