Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
LS3: a Linear Semantic Scan Statistic technique for detecting anomalous windows
Proceedings of the 2005 ACM symposium on Applied computing
FS3: A Random Walk Based Free-Form Spatial Scan Statistic for Anomalous Window Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Random Walks to Identify Anomalous Free-Form Spatial Scan Windows
IEEE Transactions on Knowledge and Data Engineering
Extracting hot spots of topics from time-stamped documents
Data & Knowledge Engineering
Event identification in web social media through named entity recognition and topic modeling
Data & Knowledge Engineering
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Anomalous windows are the contiguous groupings of data points. In this paper, we propose an approach for discovering anomalous windows using Scan Statistics for Linear Intersecting Paths (SSLIP). A linear path refers to a path represented by a line with a single dimensional spatial coordinate marking an observation point. Our approach for discovering anomalous windows along linear paths comprises of the following distinct steps: (a) Cross Path Discovery: where we identify a subset of intersecting paths to be considered, (b) Anomalous Window Discovery: where we outline three order invariant algorithms, namely SSLIP, Brute Force-SSLIP and Central Brute Force-SSLIP, for the traversal of the cross paths to identify varying size directional windows along the paths. For identifying an anomalous window we compute an unusualness metric, in the form of a likelihood ratio to indicate the degree of unusualness of this window with respect to the rest of the data. We identify the window with the highest likelihood ratio as our anomalous window, and (c) Monte Carlo Simulations: to ascertain whether this window is truly anomalous and not just a random occurrence we perform hypothesis testing by computing a p-value using Monte Carlo Simulations. We present extensive experimental results in real world accident datasets for various highways with known issues(code and data available from [27], [21]). Our results show that our approach indeed is effective in identifying anomalous traffic accident windows along multiple intersecting highways.