Mining Serial Episode Rules with Time Lags over Multiple Data Streams

Lee, Tung-Ying; Wang, En Tzu; Chen, Arbee L.

doi:10.1007/978-3-540-85836-2_22

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Our problem is closely related to the frequent episode mining problem [20], [21], [22]. Hence, from here on, we shall use the terms episodes and temporal association rules interchangeably.…”

Section: Mining Temporal Associationsmentioning

confidence: 99%

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Predictive Handling of Asynchronous Concept Drifts in Distributed Environments

Ang

Gopalkrishnan²,

Žliobaitė

et al. 2013

IEEE Trans. Knowl. Data Eng.

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In a distributed computing environment, peers collaboratively learn to classify concepts of interest from each other. When external changes happen and their concepts drift, the peers should adapt to avoid increase in misclassification errors. The problem of adaptation becomes more difficult when the changes are asynchronous, i.e., when peers experience drifts at different times. We address this problem by developing an ensemble approach, PINE, that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers. With empirical study on simulated and real-world data sets, we show that PINE handles asynchronous concept drifts better and faster than current state-of-the-art approaches, which have been designed to work in less challenging environments. In addition, PINE is parameter insensitive and incurs less communication cost while achieving better accuracy.

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Section: Mining Temporal Associationsmentioning

confidence: 99%

“…In this setting, based on the definition of serial episode [22], all the mined frequent episodes will have hundred percent confidence, which is not useful at all. Hence, we deviate from the standard definition to define confidence as the same value as support since it gives the probability a given episode leading to an LCD.…”

Section: Mining Temporal Associationsmentioning

confidence: 99%

Predictive Handling of Asynchronous Concept Drifts in Distributed Environments

Ang

Gopalkrishnan²,

Žliobaitė

et al. 2013

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…This problem is closely related to the frequent episode mining problem [HD04,LWC08,MTIV97]. Hence, from here on, the terms episodes and temporal association rules shall be used interchangeably.…”

Section: Mining Temporal Associationsmentioning

confidence: 99%

“…In this setting, based on the definition of serial episode [LWC08], all the mined frequent episodes will have hundred percent confidence, which is not useful at all. Hence, the standard definition is deviated to define confidence as the same value as support σ since it gives the probability a given episode leading to a local concept drift.…”

Section: Mining Temporal Associationsmentioning

confidence: 99%