What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Data mining
- Statistics
Data mining, Association rule learning, Data science, Representation and Construct are his primary areas of study.
His Data mining research is multidisciplinary, incorporating elements of Pruning, State, Sampling, Transaction data and Synthetic data.
His Pruning study integrates concerns from other disciplines, such as Efficient algorithm and Sequential Pattern Mining.
His Association rule learning study frequently involves adjacent topics like Information retrieval.
His Information retrieval study combines topics from a wide range of disciplines, such as Benchmark and Code.
The Data science study combines topics in areas such as Affinity analysis, Biological data and Identification.
His most cited work include:
- On private scalar product computation for privacy-preserving data mining (343 citations)
- Mining All Non-derivable Frequent Itemsets (257 citations)
- Survey on Frequent Pattern Mining (229 citations)
What are the main themes of his work throughout his whole career to date?
Bart Goethals focuses on Data mining, Association rule learning, Artificial intelligence, Information retrieval and Data science.
His work on Concept mining expands to the thematically related Data mining.
He works mostly in the field of Association rule learning, limiting it down to topics relating to Transaction data and, in certain cases, Pruning.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His Data science research includes themes of Domain knowledge, Cluster analysis and Identification.
His Data stream mining study which covers Field that intersects with Knowledge extraction.
He most often published in these fields:
- Data mining (50.90%)
- Association rule learning (20.36%)
- Artificial intelligence (20.96%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (20.96%)
- Data mining (50.90%)
- Machine learning (8.38%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Data mining, Machine learning, Recommender system and Collaborative filtering.
Bart Goethals has researched Artificial intelligence in several fields, including Industrial engineering and Pattern recognition.
His is doing research in Association rule learning and Episode mining, both of which are found in Data mining.
The various areas that Bart Goethals examines in his Machine learning study include Distance based, Classifier and Scalability.
His Recommender system study incorporates themes from Exploit, Similarity computation and Curse of dimensionality.
He combines subjects such as High dimensional, Theoretical computer science and Relevance with his study of Collaborative filtering.
Between 2016 and 2021, his most popular works were:
- Proximity forest: an effective and scalable distance-based classifier for time series (45 citations)
- Analyzing concept drift and shift from sample data (33 citations)
- Analyzing concept drift and shift from sample data (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Bart Goethals spends much of his time researching Artificial intelligence, Concept drift, Marginal distribution, Data mining and Reliability.
His Artificial intelligence study combines topics in areas such as Machine learning and Time series.
The concepts of his Machine learning study are interwoven with issues in Distance based, Classifier and Scalability.
The study incorporates disciplines such as Visualization, Scheduling and Sample in addition to Concept drift.
He performs integrative Marginal distribution and Industrial engineering research in his work.
He studies Data mining, focusing on Lift in particular.
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