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» Discovering Structural Anomalies in Graph-Based Data
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ISI
2007
Springer
13 years 11 months ago
Detecting Anomalies in Graphs
Graph data represents relationships, connections, or affinities. Innocent relationships produce repeated, and so common, substructures in graph data. We present techniques for dis...
David B. Skillicorn
INCDM
2010
Springer
204views Data Mining» more  INCDM 2010»
13 years 8 months ago
Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways
Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managin...
Jonas Poelmans, Guido Dedene, Gerda Verheyden, Her...
IPPS
2000
IEEE
13 years 9 months ago
PaDDMAS: Parallel and Distributed Data Mining Application Suite
Discovering complex associations, anomalies and patterns in distributed data sets is gaining popularity in a range of scientific, medical and business applications. Various algor...
Omer F. Rana, David W. Walker, Maozhen Li, Steven ...
KDD
2010
ACM
247views Data Mining» more  KDD 2010»
13 years 7 months ago
Metric forensics: a multi-level approach for mining volatile graphs
Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not ...
Keith Henderson, Tina Eliassi-Rad, Christos Falout...
DIMVA
2008
13 years 6 months ago
Embedded Malware Detection Using Markov n-Grams
Embedded malware is a recently discovered security threat that allows malcode to be hidden inside a benign file. It has been shown that embedded malware is not detected by commerci...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...