Fast mining and forecasting of complex time-stamped events
Fast mining and forecasting of complex time-stamped events
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DOI：
10.1145/2339530.2339577
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发表时间：
2012-08
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通讯作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos;Tomoharu Iwata;Masatoshi Yoshikawa
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作者：
Yasuko Matsubara;Yasushi Sakurai;C. Faloutsos;Tomoharu Iwata;Masatoshi Yoshikawa
考虑到大量的时间演变事件，如网络点击日志，其中包括多个属性（例如，URL、userID、times-tamp），我们如何发现模式和趋势？我们如何捕捉日常模式并预测未来事件？我们需要两个属性：（a）有效性，即模式应该帮助我们理解数据，发现组，并实现预测，以及（B）可扩展性，即方法应该与数据大小成线性关系。我们引入TriMine，它对所有三个属性（即URL，用户和时间）执行三向挖掘。具体来说，TriMine可以同时发现隐藏的主题、URL组和用户组。由于其简洁而有效的总结，它使人们有可能完成最具挑战性和最重要的任务，即预测未来事件。在真实的数据集上进行的大量实验表明，TriMine可以发现有意义的主题并进行长期预测，这是众所周知的难以实现的。事实上，TriMine在准确性和执行速度方面始终优于最先进的现有方法（高达74倍）。
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).