Computational geometry: an introduction
Computational geometry: an introduction
The design and analysis of spatial data structures
The design and analysis of spatial data structures
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
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiresolution instance-based learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The hunting of the bump: on maximizing statistical discrepancy
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Statistical change detection for multi-dimensional data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Augmented Privacy with Virtual Humans
Digital Human Modeling
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
A LRT framework for fast spatial anomaly detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Interactive Visualization of Network Anomalous Events
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Monitoring food safety by detecting patterns in consumer complaints
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Efficiently mining regional outliers in spatial data
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
A Model-Agnostic Framework for Fast Spatial Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Regional behavior change detection via local spatial scan
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatially regularized logistic regression for disease mapping on large moving populations
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
A fresh perspective: learning to sparsify for detection in massive noisy sensor networks
Proceedings of the 12th international conference on Information processing in sensor networks
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Given an N x N grid of squares, where each square has a count cij and an underlying population pij, our goal is to find the rectangular region with the highest density, and to calculate its significance by randomization. An arbitrary density function D, dependent on a region's total count C and total population P, can be used. For example, if each count represents the number of disease cases occurring in that square, we can use Kulldorff's spatial scan statistic DK to find the most significant spatial disease cluster. A naive approach to finding the maximum density region requires O(N4) time, and is generally computationally infeasible. We present a multiresolution algorithm which partitions the grid into overlapping regions using a novel overlap-kd tree data structure, bounds the maximum score of subregions contained in each region, and prunes regions which cannot contain the maximum density region. For sufficiently dense regions, this method finds the maximum density region in O((N log N)2) time, in practice resulting in significant (20-2000x) speedups on both real and simulated datasets.