Browsing by Author "Weng, Julia Tzu-Ya"

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Incremental mining of temporal patterns in interval-based database
(2016-02-01)
Hui, Lin
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Chen, Yi-Cheng
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Weng, Julia Tzu-Ya
;
Lee, Suh-Yin
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資訊工程學系
In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also called temporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.
Incrementally Mining Temporal Patterns in Interval-based Databases
(2014)
Chen, Yi-Cheng
;
Weng, Julia Tzu-Ya
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Wang, Jun-Zhe
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Chou, Chien-Li
;
Huang, Jiun-Long
In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.