@inproceedings{867a83162c2640229ab1e0fb7a7a444a,
title = "A fast algorithm for combinatorial hotspot mining based on spatial scan statistic",
abstract = "It is a popular and classical problem to detect a hotspot cluster from a statistical data which is partitioned by geographical regions such as prefectures or cities. Spatial scan statistic is a standard measure of likelihood ratio which has been widely used for testing hotspot clusters. In this work, we propose a very fast algorithm to enumerate all combinatorial regions which are more significant than a given threshold value. Our algorithm features the fast exploration by pruning the search space based on the partial monotonicity of the spatial scan statistic. Experimental results for a nation-wide 47 prefectures dataset show that our method generates the highest-ranked hotspot cluster in a time a million or more times faster than the previous naive search method. Our method works practically for a dataset with several hundreds of regions, and it will drastically accelerate hotspot analysis in various fields.",
keywords = "Combinatorial problem, Data mining, Enumeration algorithm, Hotspot detection, Scan statistic",
author = "Minato, {Shin ichi} and Jun Kawahara and Fumio Ishioka and Masahiro Mizuta and Koji Kurihara",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SIAM.; 19th SIAM International Conference on Data Mining, SDM 2019 ; Conference date: 02-05-2019 Through 04-05-2019",
year = "2019",
doi = "10.1137/1.9781611975673.11",
language = "English",
series = "SIAM International Conference on Data Mining, SDM 2019",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "91--99",
booktitle = "SIAM International Conference on Data Mining, SDM 2019",
address = "United States",
}