P
Philippe Fournier-Viger
Researcher at Harbin Institute of Technology
Publications - 350
Citations - 9986
Philippe Fournier-Viger is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Pruning (decision trees). The author has an hindex of 44, co-authored 321 publications receiving 6980 citations. Previous affiliations of Philippe Fournier-Viger include Université du Québec à Montréal & Université de Moncton.
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Book ChapterDOI
The SPMF Open-Source Data Mining Library Version 2
Philippe Fournier-Viger,Jerry Chun-Wei Lin,Antonio Gomariz,Ted Gueniche,Azadeh Soltani,Zhi-Hong Deng,Hoang Thanh Lam +6 more
TL;DR: This paper introduces the second major revision of SPMF 2, which provides more than 60 new algorithm implementations (including novel algorithms for sequence prediction), an improved user interface with pattern visualization, a novel plug-in system, improved performance, and support for text mining.
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.
Journal ArticleDOI
Binary dragonfly optimization for feature selection using time-varying transfer functions
Majdi Mafarja,Ibrahim Aljarah,Ali Asghar Heidari,Hossam Faris,Philippe Fournier-Viger,Xiaodong Li,Seyedali Mirjalili +6 more
TL;DR: A wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm based on time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation.
Book ChapterDOI
Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information
TL;DR: An extensive experimental study with six real-life datasets shows that co-occurrence-based pruning is effective, CMAP is very compact and the resulting algorithms outperform state-of-the-art algorithms for mining sequential patterns and closed sequential patterns (ClaSP and CloSpan).