The scientist’s investigation covers issues in Data mining, Set, Association rule learning, Scalability and Database transaction. His work carried out in the field of Data mining brings together such families of science as Machine learning, Task, Artificial intelligence, Pruning and Efficient algorithm. His Set research includes elements of Process and Representation.
Philippe Fournier-Viger usually deals with Association rule learning and limits it to topics linked to Extension and Antecedent, Domain and Intelligent tutoring system. His Scalability research incorporates themes from Control, Discriminative model and Skyline. His study in Database transaction is interdisciplinary in nature, drawing from both Field, Data structure, Text mining, Object and Table.
His main research concerns Data mining, Artificial intelligence, Pruning, Set and Database transaction. His work on Association rule learning as part of general Data mining study is frequently linked to Structure, therefore connecting diverse disciplines of science. His Artificial intelligence research is multidisciplinary, relying on both Domain and Machine learning.
Philippe Fournier-Viger has researched Pruning in several fields, including Tree, Space and Data structure. His research integrates issues of Algorithm, Representation and Sequential Pattern Mining in his study of Set. He performs integrative study on Database transaction and Profit.
His primary areas of investigation include Data mining, Set, Pruning, Database transaction and Theoretical computer science. He combines subjects such as Tree, Scalability and Consumer behaviour with his study of Data mining. His studies deal with areas such as Combinatorial explosion, Object, Association rule learning, Benchmark and Algorithm as well as Set.
His Association rule learning study combines topics in areas such as Class and Ranking. His research in Pruning intersects with topics in Space, Sequential Pattern Mining and Reduction. His Database transaction research incorporates elements of Range and Confidentiality.
His scientific interests lie mostly in Data mining, Set, Database transaction, Pruning and Theoretical computer science. His work carried out in the field of Data mining brings together such families of science as Information sensitivity and Consumer behaviour. He combines subjects such as Episode mining, Event sequence, Fast algorithm and Benchmark with his study of Set.
Philippe Fournier-Viger interconnects Scalability, Overhead, Dykstra's projection algorithm, Reduction and Uncertain data in the investigation of issues within Pruning. His Theoretical computer science study incorporates themes from Genetic algorithm, HOL and Mutation. The various areas that Philippe Fournier-Viger examines in his Affinity analysis study include Field, Knowledge extraction and Combinatorial explosion.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
SPMF: a Java open-source pattern mining library
Philippe Fournier-Viger;Antonio Gomariz;Ted Gueniche;Azadeh Soltani.
Journal of Machine Learning Research (2014)
FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning
Philippe Fournier-Viger;Cheng Wei Wu;Souleymane Zida;Vincent Shin-Mu Tseng.
international syposium on methodologies for intelligent systems (2014)
The SPMF Open-Source Data Mining Library Version 2
Philippe Fournier-Viger;Jerry Chun-Wei Lin;Antonio Gomariz;Ted Gueniche.
european conference on machine learning (2016)
Binary dragonfly optimization for feature selection using time-varying transfer functions
Majdi M. Mafarja;Ibrahim Aljarah;Ali Asghar Heidari;Hossam Faris.
Knowledge Based Systems (2018)
Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information
Philippe Fournier-Viger;Antonio Gomariz;Manuel Campos;Rincy Thomas.
pacific-asia conference on knowledge discovery and data mining (2014)
Efficient Algorithms for Mining Top-K High Utility Itemsets
Vincent S. Tseng;Cheng-Wei Wu;Philippe Fournier-Viger;Philip S. Yu.
IEEE Transactions on Knowledge and Data Engineering (2016)
A survey of itemset mining
Philippe Fournier‐Viger;Jerry Chun‐Wei Lin;Bay Vo;Bay Vo;Tin Truong Chi.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (2017)
EFIM: a fast and memory efficient algorithm for high-utility itemset mining
Souleymane Zida;Philippe Fournier-Viger;Jerry Chun-Wei Lin;Cheng-Wei Wu.
Knowledge and Information Systems (2017)
EFIM: A highly efficient algorithm for high-utility itemset mining
Souleymane Zida;Philippe Fournier-Viger;Jerry Chun Wei Lin;Cheng Wei Wu.
mexican international conference on artificial intelligence (2015)
CMRules: Mining sequential rules common to several sequences
Philippe Fournier-Viger;Usef Faghihi;Roger Nkambou;Engelbert Mephu Nguifo.
Knowledge Based Systems (2012)
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