UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES

DOI10.1142/S0219622010004172zbMath1207.68256MaRDI QIDQ3063633

Kuo-Wei Hsu, Yan Fu, Aleksandar Lazarevic, Yue Wu, Jaideep Srivastava, Junlin Zhou

Publication date: 15 December 2010

Published in: International Journal of Information Technology & Decision Making (Search for Journal in Brave)

zbMATH Keywords

global anomaliescombining modelsdistributed anomaly detection

Mathematics Subject Classification ID

Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• SMOTEBoost

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• A decision-theoretic generalization of on-line learning and an application to boosting
• FUZZY ART-BASED IMAGE CLUSTERING METHOD FOR CONTENT-BASED IMAGE RETRIEVAL
• PARALLEL PROCESSING OF OLAP QUERIES USING A CLUSTER OF WORKSTATIONS
• A randomized linear-time algorithm to find minimum spanning trees
• Two-phase clustering process for outliers detection
• Robust classification for imprecise environments
• Random forests

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