TY - GEN
T1 - A causal approach for mining interesting anomalies
AU - Babbar, Sakshi
AU - Surian, Didi
AU - Chawla, Sanjay
PY - 2013
Y1 - 2013
N2 - We propose a novel approach which combines the use of Bayesian network and probabilistic association rules to discover and explain anomalies in data. The Bayesian network allows us to organize information in order to capture both correlation and causality in the feature space, while the probabilistic association rules have a structure similar to association mining rules. In particular, we focus on two types of rules: (i) low support & high confidence and, (ii) high support & low confidence. New data points which satisfy either one of the two rules conditioned on the Bayesian network are the candidate anomalies. We perform extensive experiments on well-known benchmark data sets and demonstrate that our approach is able to identify anomalies in high precision and recall. Moreover, our approach can be used to discover contextual information from the mined anomalies, which other techniques often fail to do so.
AB - We propose a novel approach which combines the use of Bayesian network and probabilistic association rules to discover and explain anomalies in data. The Bayesian network allows us to organize information in order to capture both correlation and causality in the feature space, while the probabilistic association rules have a structure similar to association mining rules. In particular, we focus on two types of rules: (i) low support & high confidence and, (ii) high support & low confidence. New data points which satisfy either one of the two rules conditioned on the Bayesian network are the candidate anomalies. We perform extensive experiments on well-known benchmark data sets and demonstrate that our approach is able to identify anomalies in high precision and recall. Moreover, our approach can be used to discover contextual information from the mined anomalies, which other techniques often fail to do so.
KW - Bayesian network
KW - anomaly
KW - causality
KW - probabilistic association rules
UR - http://www.scopus.com/inward/record.url?scp=84884497790&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38457-8_20
DO - 10.1007/978-3-642-38457-8_20
M3 - Conference contribution
AN - SCOPUS:84884497790
SN - 9783642384561
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 226
EP - 232
BT - Advances in Artificial Intelligence - 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Proceedings
T2 - 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013
Y2 - 28 May 2013 through 31 May 2013
ER -