Published on Mon Jul 05 2010

A unified view of Automata-based algorithms for Frequent Episode Discovery

Avinash Achar, Srivatsan Laxman, P. S. Sastry

F Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases.

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Abstract

Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various algorithms as we show here. We also point out how this unified view helps us to consider generalization of the algorithm so that they can discover episodes with general partial orders.

Sat Feb 07 2009
Artificial Intelligence
Discovering general partial orders in event streams
Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient algorithms exist for discovering frequent episodes with general partial orders.
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Tue Apr 16 2019
Machine Learning
Mining Closed Episodes with Simultaneous Events
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.
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Mon Apr 15 2019
Machine Learning
Discovering Episodes with Compact Minimal Windows
An episode is significant if its occurrence is abnormally compact. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure.
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Mon May 21 2012
Machine Learning
Streaming Algorithms for Pattern Discovery over Dynamically Changing Event Sequences
Discovering frequent episodes over event sequences is an important data mining task. Current methods for discovering frequent episodes are typically multipass algorithms, making them unsuitable in the streaming context. Our algorithm processes events as they arrive, one batch at a time, while discovering top frequent episodes.
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Tue Mar 12 2019
Artificial Intelligence
Temporal Logics Over Finite Traces with Uncertainty (Technical Report)
Temporal logics over finite traces have recently seen wide application in business process modelling, monitoring, and mining. However, real-life dynamic systems contain adegree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic using superposition semantics.
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Sun Apr 14 2019
Machine Learning
Mining Closed Strict Episodes
Discovering patterns in a sequence is an important aspect of data mining. One popular choice of such patterns are episodes, patterns in sequential data. In this work we introduce a technique for discovering closed episodes.
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