Data mining for visual exploration and detection of ecosystem disturbances
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Spatial outlier detection: random walk based approaches
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
On detecting clustered anomalies using SCiForest
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Spectral clustering with density sensitive similarity function
Knowledge-Based Systems
Spatio-temporal outlier detection based on context: a summary of results
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Mining at most top-K% spatio-temporal outlier based context: a summary of results
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Discovery of extreme events-related communities in contrasting groups of physical system networks
Data Mining and Knowledge Discovery
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The discovery of objects with exceptional behavior is an important challenge from a knowledge discovery standpoint and has attracted much attention recently. In this paper, we present a stochastic graph-based algorithm, called OutRank, for detecting outlying objects. In our method, a matrix is constructed using the similarity between objects and used as the adjacency matrix of the graph representation. The heart of this approach is the Markov model that is built upon this graph, which assigns an outlier score to each object. Using this framework, we show that our algorithm is more powerful than the existing outlier detection schemes and can effectively address the inherent problems of such schemes. Empirical studies conducted on both real and synthetic data sets show that significant improvements in detection rate and a lower false alarm rate are achieved using our proposed framework.