Attribute outlier detection over data streams

TY - GEN

T1 - Attribute outlier detection over data streams

AU - Cao, Hui

AU - Zhou, Yongluan

AU - Shou, Lidan

AU - Chen, Gang

N1 - Conference code: 15

PY - 2010

Y1 - 2010

N2 - Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.

AB - Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.

U2 - 10.1007/978-3-642-12098-5_17

DO - 10.1007/978-3-642-12098-5_17

M3 - Article in proceedings

SN - 978-3-642-12097-8

T3 - Lecture notes in computer science

SP - 216

EP - 230

BT - Database Systems for Advanced Applications

A2 - Kitagawa, Hiroyuki

A2 - Ishikawa, Yoshiharu

A2 - Li, Qing

A2 - Watanabe, Chiemi

PB - Springer

T2 - 15th International Conference on Database Systems for Advanced Applications

Y2 - 1 April 2010 through 4 April 2010

ER -