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The Continuable Mining Approach to Mining Frequent Serial Episodes in the Event Sequence

机译:事件序列中频繁序列情节的连续挖掘方法

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In this paper, a novel approach to mining frequent serial episodes from streams of time series, is proposed. The approach is called TFSE(i.e., Time-table-joined Frequent Serial Episodes). And the conception of Episode-timetable and re-mine is put forward. An Episode-time-table is corresponding with a episode pattern. The Episode-timetable records the start-time and end-time of all of the corresponding episodes. A new Episode-time-table is generated by joining two appropriate Episode-time-tables. The amount of records in the Episode-time-table is the number of episodes occurring in the time series. Re-mine need not to scan original time series and retrench much resource and time. Because the middle results are stored and the measure of joining two appropriate Episode-time-tables is taken to test candidate episode, to a certain extent, TFSE reduces difficulty with mining long episodes.
机译:本文提出了一种从时间序列流中挖掘频繁序列情节的新方法。该方法称为TFSE(即加入时间表的连续序列情节)。提出了情节时间表和重新排雷的概念。情节时间表与情节模式相对应。情节时间表记录所有相应情节的开始时间和结束时间。通过将两个适当的情节时间表结合在一起,可以生成一个新的情节时间表。情节时间表中的记录数是时间序列中发生的情节数。重新挖掘无需扫描原始时间序列并节省大量资源和时间。因为存储了中间结果,并且采取了将两个适当的插值时间表相结合的方法来测试候选插值,所以在一定程度上,TFSE减少了挖掘较长插值的难度。

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