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Filtering and refinement: a two-stage approach for efficient and effective anomaly detection

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Title: Filtering and refinement: a two-stage approach for efficient and effective anomaly detection
Author(s): Yu, Xiao
Advisor(s): Han, Jiawei
Department / Program: Computer Science
Discipline: Computer Science
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(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.
Issue Date: 2011-05-25
URI: http://hdl.handle.net/2142/24511
Rights Information: Copyright 2011 Xiao Yu
Date Available in IDEALS: 2011-05-25
2013-05-26
Date Deposited: 2011-05
 

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