V
Vincent S. Tseng
Researcher at National Chiao Tung University
Publications - 322
Citations - 10828
Vincent S. Tseng is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Association rule learning & Cluster analysis. The author has an hindex of 50, co-authored 321 publications receiving 9051 citations. Previous affiliations of Vincent S. Tseng include University of California, Los Angeles & Harbin Institute of Technology.
Papers
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Journal ArticleDOI
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
TL;DR: Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.
Proceedings ArticleDOI
UP-Growth: an efficient algorithm for high utility itemset mining
TL;DR: The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions.
Journal Article
SPMF: a Java open-source pattern mining library
Philippe Fournier-Viger,Antonio Gomariz,Ted Gueniche,Azadeh Soltani,Cheng-Wei Wu,Vincent S. Tseng +5 more
TL;DR: SPMF is an open-source data mining library offering implementations of more than 55 data mining algorithms, specialized for discovering patterns in transaction and sequence databases such as frequent itemsets, association rules and sequential patterns.
Book ChapterDOI
FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning
TL;DR: An extensive experimental study with four real-life datasets shows that the resulting algorithm named FHM (Fast High-Utility Miner) reduces the number of join operations by up to 95 % and is up to six times faster than the state-of-the-art algorithm HUI-Miner.
Proceedings ArticleDOI
Semantic trajectory mining for location prediction
TL;DR: The core idea of the prediction model is a novel cluster-based prediction strategy which evaluates the next location of a mobile user based on the frequent behaviors of similar users in the same cluster determined by analyzing users' common behavior in semantic trajectories.