Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Bump hunting in high-dimensional data
Statistics and Computing
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Detecting research topics via the correlation between graphs and texts
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
A bayesian mixture model with linear regression mixing proportions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Simulation of Multivariate Spatial-Temporal Outbreak Data for Detection Algorithm Evaluation
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Change analysis in spatial datasets by interestingness comparison
SIGSPATIAL Special
Guessing the extreme values in a data set: a Bayesian method and its applications
The VLDB Journal — The International Journal on Very Large Data Bases
Data Mining and Knowledge Discovery
Spatio-temporal clustering of road network data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Annals of Mathematics and Artificial Intelligence
Discovering emerging topics in unlabelled text collections
ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems
On mining anomalous patterns in road traffic streams
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
SigSpot: mining significant anomalous regions from time-evolving networks (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
On detection of emerging anomalous traffic patterns using GPS data
Data & Knowledge Engineering
Fast generalized subset scan for anomalous pattern detection
The Journal of Machine Learning Research
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We propose a new class of spatio-temporal cluster detection methods designed for the rapid detection of emerging space-time clusters. We focus on the motivating application of prospective disease surveillance: detecting space-time clusters of disease cases resulting from an emerging disease outbreak. Automatic, real-time detection of outbreaks can enable rapid epidemiological response, potentially reducing rates of morbidity and mortality. Building on the prior work on spatial and space-time scan statistics, our methods combine time series analysis (to determine how many cases we expect to observe for a given spatial region in a given time interval) with new "emerging cluster" space-time scan statistics (to decide whether an observed increase in cases in a region is significant), enabling fast and accurate detection of emerging outbreaks. We evaluate these methods on two types of simulated outbreaks: aerosol release of inhalational anthrax (e.g. from a bioterrorist attack) and FLOO ("Fictional Linear Onset Outbreak"), injected into actual baseline data (Emergency Department records and over-the-counter drug sales data from Allegheny County). We demonstrate that our methods are successful in rapidly detecting both outbreak types while keeping the number of false positives low, and show that our new "emerging cluster" scan statistics consistently outperform the standard "persistent cluster" scan statistics approach.