Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Multi-scale temporal segmentation and outlier detection in sensor networks
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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A key goal of information analytics is to identify patterns of anomalous behavior. Such identification of anomalies is required in a variety of applications such as systems management, sensor networks, and security. However, most of the current state of the art on anomaly detection relies on using a predefined knowledge base. This knowledge base may consist of a predefined set of policies and rules, a set of templates representing predefined patterns in the data, or a description of events that constitutes anomalous behavior. When used in practice, a significant limitation of information analytics is the effort that goes into defining and creating the predefined knowledge base and the need to have prior information about the domain. In this paper, we present an approach that can identify anomalies in the information stream without requiring any prior domain knowledge. The proposed approach simultaneously monitors and analyzes the data stream at multiple temporal scales and learns the evolution of normal behavior over time in each time scale. The proposed approach is not sensitive to the choice of the distance metric and hence is applicable in various domains and applications. We have studied the effectiveness of the approach using different data sets.