H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 41 Citations 7,270 277 World Ranking 4339 National Ranking 401

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Data mining

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 most cited work include:

  • SPMF: a Java open-source pattern mining library (287 citations)
  • The SPMF Open-Source Data Mining Library Version 2 (218 citations)
  • FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning (216 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Data mining (60.00%)
  • Artificial intelligence (22.30%)
  • Pruning (19.67%)

What were the highlights of his more recent work (between 2019-2021)?

  • Data mining (60.00%)
  • Set (18.69%)
  • Pruning (19.67%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • HUOPM: High-Utility Occupancy Pattern Mining (59 citations)
  • A Survey of Utility-Oriented Pattern Mining (47 citations)
  • Fast Utility Mining on Sequence Data (14 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

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.

Top Publications

SPMF: a Java open-source pattern mining library

Philippe Fournier-Viger;Antonio Gomariz;Ted Gueniche;Azadeh Soltani.
Journal of Machine Learning Research (2014)

450 Citations

FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning

Philippe Fournier-Viger;Cheng-Wei Wu;Souleymane Zida;Vincent S. Tseng.
international syposium on methodologies for intelligent systems (2014)

332 Citations

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)

302 Citations

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)

238 Citations

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)

190 Citations

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)

173 Citations

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)

153 Citations

CMRules: Mining sequential rules common to several sequences

Philippe Fournier-Viger;Usef Faghihi;Roger Nkambou;Engelbert Mephu Nguifo.
Knowledge Based Systems (2012)

119 Citations

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)

116 Citations

RuleGrowth: mining sequential rules common to several sequences by pattern-growth

Philippe Fournier-Viger;Roger Nkambou;Vincent Shin-Mu Tseng.
acm symposium on applied computing (2011)

112 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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