H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 184 Citations 205,519 993 World Ranking 3 National Ranking 2

Research.com Recognitions

Awards & Achievements

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.

Overview

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.

Best Publications

Data Mining: Concepts and Techniques

Jiawei Han;Micheline Kamber;Jian Pei.
(2000)

49192 Citations

Mining frequent patterns without candidate generation

Jiawei Han;Jian Pei;Yiwen Yin.
international conference on management of data (2000)

9197 Citations

Data mining: an overview from a database perspective

Ming-Syan Chen;Jiawei Han;P.S. Yu.
IEEE Transactions on Knowledge and Data Engineering (1996)

3543 Citations

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)

3118 Citations

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)

3070 Citations

gSpan: graph-based substructure pattern mining

Xifeng Yan;Jiawei Han.
international conference on data mining (2002)

2680 Citations

Efficient and Effective Clustering Methods for Spatial Data Mining

Raymond T. Ng;Jiawei Han.
very large data bases (1994)

2644 Citations

Data Mining: Concepts and Techniques (2nd edition)

Jiawei Han;Micheline Kamber.
(2006)

2370 Citations

A framework for clustering evolving data streams

Charu C. Aggarwal;Jiawei Han;Jianyong Wang;Philip S. Yu.
very large data bases (2003)

2309 Citations

CMAR: accurate and efficient classification based on multiple class-association rules

Wenmin Li;Jiawei Han;Jian Pei.
international conference on data mining (2001)

1704 Citations

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Best Scientists Citing Jiawei Han

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

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Tzung-Pei Hong

Tzung-Pei Hong

National University of Kaohsiung

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Charu C. Aggarwal

Charu C. Aggarwal

IBM (United States)

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Philippe Fournier-Viger

Philippe Fournier-Viger

Harbin Institute of Technology

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Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

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Christos Faloutsos

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Carnegie Mellon University

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Carson Kai-Sang Leung

Carson Kai-Sang Leung

University of Manitoba

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Xindong Wu

Xindong Wu

Hefei University of Technology

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David Lo

David Lo

Singapore Management University

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Alfredo Cuzzocrea

Alfredo Cuzzocrea

University of Calabria

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Vincent S. Tseng

Vincent S. Tseng

National Yang Ming Chiao Tung University

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Reda Alhajj

Reda Alhajj

University of Calgary

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Lei Chen

Lei Chen

Hong Kong University of Science and Technology

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Jian Pei

Jian Pei

Simon Fraser University

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Dacheng Tao

Dacheng Tao

University of Sydney

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Yong Shi

Yong Shi

Chinese Academy of Sciences

Publications: 101

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