2012 FastMiningandForecastingofCompl

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Notes

Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, times - tamp), how do we find patterns and trends? How do we go about capturing daily patterns and forecasting future events? We need two properties: (a) effectiveness, that is, the patterns should help us understand the data, discover groups, and enable forecasting, and (b) scalability, that is, the method should be linear with the data size. We introduce TriMine, which performs three-way mining for all three attributes, namely, URLs, users, and time. Specifically TriMine discovers hidden topics, groups of URLs, and groups of users, simultaneously. Thanks to its concise but effective summarization, it makes it possible to accomplish the most challenging and important task, namely, to forecast future events. Extensive experiments on real datasets demonstrate that TriMine discovers meaningful topics and makes long-range forecasts, which are notoriously difficult to achieve. In fact, TriMine consistently outperforms the best state-of-the-art existing methods in terms of accuracy and execution speed (up to 74x faster).

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 FastMiningandForecastingofCompl	Christos Faloutsos Tomoharu Iwata Yasushi Sakurai Yasuko Matsubara Masatoshi Yoshikawa			Fast Mining and Forecasting of Complex Time-stamped Events				10.1145/2339530.2339577		2012