2009 - IEEE Fellow For contributions to data mining and knowledge discovery
2009 - W. Wallace McDowell Award, IEEE Computer Society For significant contributions to knowledge discovery and data mining.
2004 - Edward J. McCluskey Technical Achievement Award, IEEE Computer Society For contributions in data mining and knowledge discovery, data warehousing, deductive and object-oriented databases
2003 - ACM Fellow For contributions in knowledge discovery and data mining.
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 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.
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
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.
Data Mining: Concepts and Techniques
Jiawei Han;Micheline Kamber;Jian Pei.
Mining frequent patterns without candidate generation
Jiawei Han;Jian Pei;Yiwen Yin.
international conference on management of data (2000)
Data mining: an overview from a database perspective
Ming-Syan Chen;Jiawei Han;P.S. Yu.
IEEE Transactions on Knowledge and Data Engineering (1996)
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
Jian Pei;Jiawei Han;B. Mortazavi-Asl;H. Pinto.
international conference on data engineering (2001)
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Jiawei Han;Jian Pei;Yiwen Yin;Runying Mao.
Data Mining and Knowledge Discovery (2004)
gSpan: graph-based substructure pattern mining
Xifeng Yan;Jiawei Han.
international conference on data mining (2002)
Efficient and Effective Clustering Methods for Spatial Data Mining
Raymond T. Ng;Jiawei Han.
very large data bases (1994)
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han;Micheline Kamber.
A framework for clustering evolving data streams
Charu C. Aggarwal;Jiawei Han;Jianyong Wang;Philip S. Yu.
very large data bases (2003)
CMAR: accurate and efficient classification based on multiple class-association rules
Wenmin Li;Jiawei Han;Jian Pei.
international conference on data mining (2001)
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