TY - CHAP
T1 - A multi-graph spectral framework for mining multi-source anomalies
AU - Gao, Jing
AU - Du, Nan
AU - Fan, Wei
AU - Turaga, Deepak
AU - Parthasarathy, Srinivasan
AU - Han, Jiawei
N1 - Publisher Copyright:
© Springer Science+Business Media New York 2013.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Anomaly detection refers to the task of detecting objects whose characteristics deviate significantly from the majority of the data [5]. It is widely used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today's information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.
AB - Anomaly detection refers to the task of detecting objects whose characteristics deviate significantly from the majority of the data [5]. It is widely used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today's information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.
UR - http://www.scopus.com/inward/record.url?scp=84901987860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901987860&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-4457-2_9
DO - 10.1007/978-1-4614-4457-2_9
M3 - Chapter
AN - SCOPUS:84901987860
SN - 9781461444565
SP - 205
EP - 227
BT - Graph Embedding for Pattern Analysis
PB - Springer
ER -