What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Donato Malerba mostly deals with Artificial intelligence, Data mining, Machine learning, Inductive logic programming and Set.
His Artificial intelligence research includes themes of Pattern recognition, Task and Natural language processing.
Donato Malerba combines Data mining and Process modeling in his research.
In the subject of general Machine learning, his work in Decision tree, Naive Bayes classifier and Cluster analysis is often linked to Process mining, thereby combining diverse domains of study.
His research in Inductive logic programming intersects with topics in Theoretical computer science, Description logic, Operator, Order and Association rule learning.
His study in the field of Closure is also linked to topics like Posterior probability.
His most cited work include:
- Process Mining Manifesto (684 citations)
- A comparative analysis of methods for pruning decision trees (411 citations)
- Transforming paper documents into XML format with WISDOM (119 citations)
What are the main themes of his work throughout his whole career to date?
Donato Malerba mainly investigates Artificial intelligence, Data mining, Machine learning, Information retrieval and Set.
His Artificial intelligence research incorporates elements of Natural language processing, Task and Pattern recognition.
The study incorporates disciplines such as Wireless sensor network, Spatial database, Spatial analysis and Cluster analysis in addition to Data mining.
His study in Machine learning is interdisciplinary in nature, drawing from both Probabilistic logic and Autocorrelation.
The concepts of his Information retrieval study are interwoven with issues in Web page and Web mining.
His Set study frequently draws connections between adjacent fields such as Structure.
He most often published in these fields:
- Artificial intelligence (40.81%)
- Data mining (35.95%)
- Machine learning (22.43%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (40.81%)
- Data mining (35.95%)
- Machine learning (22.43%)
In recent papers he was focusing on the following fields of study:
Donato Malerba mainly focuses on Artificial intelligence, Data mining, Machine learning, Cluster analysis and Pattern recognition.
His Data mining study combines topics in areas such as Spatial analysis, Wireless sensor network, Set, STREAMS and Autocorrelation.
His Spatial analysis research incorporates themes from Co-training and Classifier.
Donato Malerba focuses mostly in the field of Set, narrowing it down to matters related to Data modeling and, in some cases, Data science.
As part of the same scientific family, Donato Malerba usually focuses on Machine learning, concentrating on Empirical research and intersecting with Active learning, Property and Software.
In general Cluster analysis, his work in Fuzzy clustering is often linked to Heterogeneous network linking many areas of study.
Between 2015 and 2021, his most popular works were:
- Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction? (42 citations)
- CloFAST: closed sequential pattern mining using sparse and vertical id-lists (34 citations)
- A Co-Training Strategy for Multiple View Clustering in Process Mining (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Pattern recognition, Machine learning and Cluster analysis.
Donato Malerba has researched Data mining in several fields, including Spatial analysis, Wireless sensor network, Evolving networks, Event and Autocorrelation.
Artificial intelligence is closely attributed to Set in his research.
His work in the fields of Hyperspectral imaging overlaps with other areas such as Space and Focus.
His Machine learning study frequently draws connections between related disciplines such as Malware.
His Process mining research includes elements of Process modeling and Business process discovery.
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