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.
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