What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
Pierpaolo D'Urso mostly deals with Fuzzy clustering, Fuzzy logic, Data mining, Cluster analysis and Pattern recognition.
His research in Fuzzy clustering intersects with topics in Type-2 fuzzy sets and systems and Medoid.
His research related to Fuzzy set, Fuzzy set operations, Defuzzification, Fuzzy classification and Fuzzy number might be considered part of Fuzzy logic.
The study incorporates disciplines such as Total least squares, Iteratively reweighted least squares, Least trimmed squares, Generalized least squares and Robust regression in addition to Data mining.
His work carried out in the field of Cluster analysis brings together such families of science as Multivariate statistics and Autocorrelation.
The various areas that he examines in his Pattern recognition study include Artificial intelligence and Time series.
His most cited work include:
- Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data (134 citations)
- Least squares estimation of a linear regression model with LR fuzzy response (126 citations)
- A least-squares approach to fuzzy linear regression analysis (97 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Cluster analysis, Artificial intelligence, Fuzzy clustering, Fuzzy logic and Data mining.
Pierpaolo D'Urso studied Cluster analysis and Outlier that intersect with Mixture model.
The concepts of his Artificial intelligence study are interwoven with issues in Sample, Machine learning and Pattern recognition.
His study in the field of FLAME clustering also crosses realms of Contiguity.
His Fuzzy logic study incorporates themes from Statistics and Multivariate statistics.
His Data mining research incorporates elements of Correlation clustering and k-medians clustering.
He most often published in these fields:
- Cluster analysis (48.03%)
- Artificial intelligence (40.16%)
- Fuzzy clustering (39.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Cluster analysis (48.03%)
- Artificial intelligence (40.16%)
- Pattern recognition (26.77%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Cluster analysis, Artificial intelligence, Pattern recognition, Fuzzy clustering and Fuzzy logic.
His Cluster analysis research includes themes of Multivariate statistics and Outlier.
His Artificial intelligence study integrates concerns from other disciplines, such as Analytics and Ordinal data.
Pierpaolo D'Urso interconnects Time series classification, Relevance and Measure in the investigation of issues within Pattern recognition.
His studies examine the connections between Fuzzy clustering and genetics, as well as such issues in Segmentation, with regards to Profiling, Data science, Market segmentation and Machine learning.
His Fuzzy logic research integrates issues from Regression analysis and Statistics.
Between 2018 and 2021, his most popular works were:
- Time Series Clustering and Classification (18 citations)
- Fuzzy clustering of mixed data (14 citations)
- Fuzzy clustering with spatial–temporal information (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of investigation include Cluster analysis, Fuzzy clustering, Pattern recognition, Artificial intelligence and Fuzzy logic.
The Cluster analysis study combines topics in areas such as Dynamic time warping, Multivariate statistics and Outlier.
His biological study deals with issues like Medoid, which deal with fields such as Benchmark and Data mining.
His Pattern recognition study integrates concerns from other disciplines, such as Relevance and Measure.
His work in the fields of Hierarchical clustering overlaps with other areas such as Bandwidth, Geographic coordinate system and Geostatistics.
His work on Fuzzy number and Fuzzy set as part of general Fuzzy logic research is often related to Microdata, thus linking different fields of science.
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