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PAKDD
2010
ACM
169views Data Mining» more  PAKDD 2010»
14 years 20 days ago
oddball: Spotting Anomalies in Weighted Graphs
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nod...
Leman Akoglu, Mary McGlohon, Christos Faloutsos
AUSDM
2007
Springer
84views Data Mining» more  AUSDM 2007»
14 years 2 months ago
Detecting Anomalous Longitudinal Associations Through Higher Order Mining
The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it...
Ping Liang, John F. Roddick
ICDM
2009
IEEE
223views Data Mining» more  ICDM 2009»
14 years 2 months ago
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
Abstract -- Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies ...
Qiang Fu, Jian-Guang Lou, Yi Wang, Jiang Li
CVPR
2010
IEEE
14 years 4 months ago
Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes
A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique ut...
Shandong Wu, Brian E. Moore, and Mubarak Shah
CORR
2010
Springer
167views Education» more  CORR 2010»
13 years 8 months ago
The DCA:SOMe Comparison A comparative study between two biologically-inspired algorithms
The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation ...
Julie Greensmith, Jan Feyereisl, Uwe Aickelin