What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Data mining
- Machine learning
His primary areas of investigation include Data mining, Artificial intelligence, Machine learning, Cluster analysis and Association rule learning.
His Data mining research includes themes of Scalability, Database and Set.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Subspace topology and Facial recognition system.
His work deals with themes such as Classifier and Training set, which intersect with Machine learning.
His study in Association rule learning is interdisciplinary in nature, drawing from both Database transaction and Transaction processing.
The study incorporates disciplines such as Relational database, Information retrieval and Data science in addition to Knowledge extraction.
His most cited work include:
- Data Mining: Concepts and Techniques (21894 citations)
- Mining frequent patterns without candidate generation (5506 citations)
- Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2052 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Artificial intelligence, Machine learning, Information retrieval and Cluster analysis.
His biological study spans a wide range of topics, including Scalability and Set.
His Artificial intelligence research incorporates themes from Pattern recognition, Task and Natural language processing.
His studies in Information retrieval integrate themes in fields like Ranking and Text mining.
In his research on the topic of Cluster analysis, Graph and Graph is strongly related with Theoretical computer science.
As part of his studies on Knowledge extraction, he often connects relevant subjects like Data science.
He most often published in these fields:
- Data mining (38.94%)
- Artificial intelligence (32.41%)
- Machine learning (16.41%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (32.41%)
- Natural language processing (8.41%)
- Embedding (5.88%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Embedding, Information retrieval and Machine learning.
His research in Artificial intelligence intersects with topics in Named-entity recognition, Task and Set.
In his research, Knowledge extraction is intimately related to Class, which falls under the overarching field of Set.
His Embedding research also works with subjects such as
- Theoretical computer science and related Graph, Graph, Node, Cluster analysis and Similarity,
- Topic model that connect with fields like Space.
His Automatic summarization study in the realm of Information retrieval interacts with subjects such as Hierarchy.
Machine learning is closely attributed to Sequence labeling in his research.
Between 2017 and 2021, his most popular works were:
- On the Variance of the Adaptive Learning Rate and Beyond. (298 citations)
- On the Variance of the Adaptive Learning Rate and Beyond (160 citations)
- Automated Phrase Mining from Massive Text Corpora (136 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Jiawei Han spends much of his time researching Artificial intelligence, Machine learning, Embedding, Natural language processing and Information retrieval.
His work carried out in the field of Artificial intelligence brings together such families of science as Named-entity recognition and Task.
His Machine learning research includes elements of Structure, Inference and Sequence labeling.
He interconnects Text corpus, Theoretical computer science and Set in the investigation of issues within Embedding.
The concepts of his Set study are interwoven with issues in Cube, Discriminative model and Data mining, Data cube.
His Data mining research integrates issues from Class and Resource.
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.
