A novel approach for mining high‐utility sequential patterns in sequence databases
CF Ahmed, SK Tanbeer, BS Jeong - ETRI journal, 2010 - Wiley Online Library
Mining sequential patterns is an important research issue in data mining and knowledge
discovery with broad applications. However, the existing sequential pattern mining
approaches consider only binary frequency values of items in sequences and equal
importance/significance values of distinct items. Therefore, they are not applicable to
actually represent many real‐world scenarios. In this paper, we propose a novel framework
for mining high‐utility sequential patterns for more real‐life applicable information extraction …
discovery with broad applications. However, the existing sequential pattern mining
approaches consider only binary frequency values of items in sequences and equal
importance/significance values of distinct items. Therefore, they are not applicable to
actually represent many real‐world scenarios. In this paper, we propose a novel framework
for mining high‐utility sequential patterns for more real‐life applicable information extraction …
A Novel Approach for Mining High-Utility Sequential Patterns in Sequence Databases Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, and Byeong-Soo …
SK Tanbeer, BS Jeong - ETRI journal, 2010 - kci.go.kr
Mining sequential patterns is an important research issue in data mining and knowledge
discovery with broad applications. However, the existing sequential pattern mining
approaches consider only binary frequency values of items in sequences and equal
importance/significance values of distinct items. Therefore, they are not applicable to
actually represent many real-world scenarios. In this paper, we propose a novel framework
for mining high-utility sequential patterns for more real-life applicable information extraction …
discovery with broad applications. However, the existing sequential pattern mining
approaches consider only binary frequency values of items in sequences and equal
importance/significance values of distinct items. Therefore, they are not applicable to
actually represent many real-world scenarios. In this paper, we propose a novel framework
for mining high-utility sequential patterns for more real-life applicable information extraction …
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