What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Xiaowei Xu spends much of his time researching Cluster analysis, Data mining, DBSCAN, OPTICS algorithm and CURE data clustering algorithm.
His research in Cluster analysis intersects with topics in Distributed computing and Database, Identification.
His Database study combines topics from a wide range of disciplines, such as Distributed algorithm, Determining the number of clusters in a data set and Parallel algorithm.
Xiaowei Xu focuses mostly in the field of Data mining, narrowing it down to matters related to Artificial intelligence and, in some cases, Machine learning and Active learning.
His research investigates the link between DBSCAN and topics such as SUBCLU that cross with problems in Algorithm and Single-linkage clustering.
His research on CURE data clustering algorithm focuses in particular on Data stream clustering.
His most cited work include:
- A density-based algorithm for discovering clusters in large spatial Databases with Noise (12123 citations)
- Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications (986 citations)
- A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise (935 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Artificial intelligence, Cluster analysis, Machine learning and CURE data clustering algorithm.
His Data mining research includes themes of DBSCAN, Algorithm, Collaborative filtering and Latent Dirichlet allocation.
The Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition.
Xiaowei Xu combines subjects such as Database and Complex network with his study of Cluster analysis.
He works on CURE data clustering algorithm which deals in particular with Data stream clustering.
Xiaowei Xu works mostly in the field of OPTICS algorithm, limiting it down to topics relating to SUBCLU and, in certain cases, Determining the number of clusters in a data set, as a part of the same area of interest.
He most often published in these fields:
- Data mining (38.98%)
- Artificial intelligence (33.05%)
- Cluster analysis (30.51%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (33.05%)
- Machine learning (16.10%)
- Pattern recognition (9.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Spamming and Segmentation.
His work on Deep learning and Feature as part of general Artificial intelligence study is frequently connected to Generator and Metric, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His study looks at the relationship between Machine learning and fields such as Lifelong learning, as well as how they intersect with chemical problems.
His work on Sparse approximation and Anomaly detection as part of general Pattern recognition research is frequently linked to Norm and Rich Text Format, bridging the gap between disciplines.
In his study, Fake reviews is inextricably linked to Data science, which falls within the broad field of Spamming.
The study incorporates disciplines such as Learning based, Differential evolution and Cluster analysis in addition to Reinforcement learning.
Between 2017 and 2021, his most popular works were:
- Graph-based review spammer group detection (35 citations)
- A hybrid temporal association rules mining method for traffic congestion prediction (16 citations)
- A hybrid temporal association rules mining method for traffic congestion prediction (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Data mining
Artificial intelligence, Machine learning, Intelligent transportation system, Data mining and Classifier are his primary areas of study.
His Visualization, Segmentation and Semantics study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Domain adaptation, bridging the gap between disciplines.
His study in the fields of Feature under the domain of Machine learning overlaps with other disciplines such as Task analysis, Human learning and Metric.
His studies deal with areas such as Annotation and Reinforcement learning as well as Feature.
Intelligent transportation system combines with fields such as Association rule learning, DBSCAN and Traffic congestion in his work.
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