UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES
| Autor: | Junlin Zhou, Yue Wu, Kuo Wei Hsu, Aleksandar Lazarevic, Jaideep Srivastava, Yan Fu |
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| Rok vydání: | 2010 |
| Předmět: |
Measure (data warehouse)
Computer science business.industry media_common.quotation_subject Global anomaly Pattern recognition Reuse computer.software_genre Data set Distributed anomaly detection global anomalies combining models Computer Science (miscellaneous) Overhead (computing) Unsupervised learning Anomaly detection Quality (business) Data mining Artificial intelligence business computer media_common |
| Zdroj: | International Journal of Information Technology and Decision Making. (06):935-957 |
| Popis: | Anomaly detection has recently become an important problem in many industrial and financial applications. Very often, the databases from which anomalies have to be found are located at multiple local sites and cannot be merged due to privacy reasons or communication overhead. In this paper, a novel general framework for distributed anomaly detection is proposed. The proposed method consists of three steps: (i) building local models for distributed data sources with unsupervised anomaly detection methods and computing quality measure of local models; (ii) transforming local unsupervised local models into sharing models; and (iii) reusing sharing models for new data and combining their results by considering both quality and diversity of them to detect anomalies in a global view. In experiments performed on synthetic and real-life large data set, the proposed distributed anomaly detection method achieved prediction performance comparable or even slightly better than the global anomaly detection algorithm applied on the data set obtained when all distributed data set were merged. |
| Databáze: | OpenAIRE |
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