What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Jian Pei focuses on Data mining, Artificial intelligence, Set, Theoretical computer science and Scalability.
His Data mining research is multidisciplinary, relying on both Data structure and Data set.
Jian Pei focuses mostly in the field of Data structure, narrowing it down to matters related to Data stream mining and, in some cases, Apriori algorithm.
Jian Pei combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
In his work, Semantics, Group and Benchmark is strongly intertwined with Space, which is a subfield of Set.
His research integrates issues of Structure, Skyline, Graph embedding and Data cube in his study of Theoretical computer science.
His most cited work include:
- Mining frequent patterns without candidate generation (5506 citations)
- Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2052 citations)
- PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth (1502 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Artificial intelligence, Information retrieval, Set and Scalability.
Jian Pei has researched Data mining in several fields, including Data set, Data structure and Cluster analysis.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His Set research is multidisciplinary, incorporating elements of Object and Algorithm.
His research in Scalability intersects with topics in Theoretical computer science and Skyline.
The concepts of his Data cube study are interwoven with issues in Data warehouse and Online analytical processing.
He most often published in these fields:
- Data mining (45.76%)
- Artificial intelligence (15.63%)
- Information retrieval (13.39%)
What were the highlights of his more recent work (between 2016-2021)?
- Theoretical computer science (12.28%)
- Artificial intelligence (15.63%)
- Set (13.17%)
In recent papers he was focusing on the following fields of study:
Theoretical computer science, Artificial intelligence, Set, Data science and Algorithm are his primary areas of study.
His Theoretical computer science study incorporates themes from Scalability, Structure, Graph, Node and Point.
Jian Pei has included themes like Machine learning, Pattern recognition and Natural language processing in his Artificial intelligence study.
His Set study integrates concerns from other disciplines, such as Consistency, Interpretability and Skyline.
His work carried out in the field of Algorithm brings together such families of science as Upper and lower bounds and Bounded function.
His biological study spans a wide range of topics, including Dimension and Data mining.
Between 2016 and 2021, his most popular works were:
- A Survey on Network Embedding (458 citations)
- Community preserving network embedding (387 citations)
- Arbitrary-Order Proximity Preserved Network Embedding (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
The scientist’s investigation covers issues in Theoretical computer science, Artificial intelligence, Machine learning, Node and Point.
His Theoretical computer science research integrates issues from Structure, Interpretation and Graph.
His research investigates the connection with Artificial intelligence and areas like Graph which intersect with concerns in Pattern recognition, Conditional random field and Deep learning.
In the field of Machine learning, his study on Cluster analysis, Recurrent neural network and Time series overlaps with subjects such as Quantile.
His Data science research includes elements of Scheme and Data mining.
His work deals with themes such as CURE data clustering algorithm, Correlation clustering, Constrained clustering, Consensus clustering and Clustering high-dimensional data, which intersect with Data mining.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
