Cluster Sequence Mining: Causal Inference with Time and Space Proximity under Uncertainty
Cluster Sequence Mining: Causal Inference with Time and Space Proximity under Uncertainty
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聚类序列挖掘：不确定性下时空邻近性的因果推断
DOI：
10.1007/978-3-319-18032-8_23
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
and Masayuki Numao
中科院分区：
文献类型：
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作者：
Yoshiyuki Okada;Ken-ichi Fukui;Koichi Moriyama;and Masayuki Numao
我们提出了一种用于数值多维事件序列的模式挖掘算法，称为聚类序列挖掘（CSM）。 CSM 提取具有一对簇的模式，该模式满足各个簇的空间接近度和来自不同簇的事件之间的时间间隔的时间接近度。 CSM 是一种独特算法（共现聚类挖掘（CCM））的扩展，考虑了事件的顺序和时间间隔的分布。时间间隔的概率密度是通过利用贝叶斯推理来推断的，以实现针对不确定性的鲁棒性。在使用合成数据的实验中，我们证实即使在不确定的情况下，CSM 也能够提取具有高 F 测量值和低时间间隔分布估计误差的簇。 CSM 被应用于 2011 年东北地震后日本的地震事件序列，以推断地震发生的因果关系。结果表明，CSM 表明俯冲带中存在一些远离东北地震主震的高影响/受影响区域。
We propose a pattern mining algorithm for numerical multidimensional event sequences, called cluster sequence mining (CSM). CSM extracts patterns with a pair of clusters that satisfies space proximity of the individual clusters and time proximity in time intervals between events from different clusters. CSM is an extension of a unique algorithm (co-occurrence cluster mining (CCM)), considering the order of events and the distribution of time intervals. The probability density of the time intervals is inferred by utilizing Bayesian inference for robustness against uncertainty. In an experiment using synthetic data, we confirmed that CSM is capable of extracting clusters with high F-measure and low estimation error of the time interval distribution even under uncertainty. CSM was applied to an earthquake event sequence in Japan after the 2011 Tohoku Earthquake to infer causality of earthquake occurrences. The results demonstrate that CSM suggests some high affecting/affected areas in the subduction zone farther away from the main shock of the Tohoku Earthquake.