NDLI: Mining closed episodes with simultaneous events

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Mining closed episodes with simultaneous events

Content Provider	ACM Digital Library
Author	Cule, Boris Tatti, Nikolaj
Abstract	Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns describing events that often occur in the vicinity of each other. Episodes can impose restrictions to the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial, parallel, and injective episodes, while discovering general episodes is understudied. In this paper we extend the definition of an episode in order to be able to represent cases where events often occur simultaneously. We present an efficient and novel miner for discovering frequent and closed general episodes. Such a task presents unique challenges. Firstly, we cannot define closure based on frequency. We solve this by computing a more conservative closure that we use to reduce the search space and discover the closed episodes as a postprocessing step. Secondly, episodes are traditionally presented as directed acyclic graphs. We argue that this representation has drawbacks leading to redundancy in the output. We solve these drawbacks by defining a subset relationship in such a way that allows us to remove the redundant episodes. We demonstrate the efficiency of our algorithm and the need for using closed episodes empirically on synthetic and real-world datasets.
Starting Page	1172
Ending Page	1180
Page Count	9
File Format	PDF
ISBN	9781450308137
DOI	10.1145/2020408.2020589
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Closed episodes Depth-first search Frequent episodes
Content Type	Text
Resource Type	Article

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