H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 163 Citations 127,284 1,531 World Ranking 7 National Ranking 4

Research.com Recognitions

Awards & Achievements

1997 - ACM Fellow For contributions to the theory and practice of analytical performance modeling of database sytems.

1993 - IEEE Fellow For contributions to the theory and practice of analytical performance modeling of database systems.


What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • The Internet

His primary scientific interests are in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Set. Philip S. Yu combines subjects such as Data stream, Clustering high-dimensional data and Graph with his study of Data mining. His studies in Graph integrate themes in fields like Graph and Theoretical computer science.

Much of his study explores Artificial intelligence relationship to Pattern recognition. Cluster analysis and Data set are frequently intertwined in his study. His work on Pruning expands to the thematically related Set.

His most cited work include:

  • Top 10 algorithms in data mining (3313 citations)
  • Data mining: an overview from a database perspective (1894 citations)
  • A framework for clustering evolving data streams (1459 citations)

What are the main themes of his work throughout his whole career to date?

Philip S. Yu spends much of his time researching Data mining, Artificial intelligence, Machine learning, Theoretical computer science and Information retrieval. His work carried out in the field of Data mining brings together such families of science as Data stream, Structure, Set and Cluster analysis. His Artificial intelligence research includes elements of Natural language processing and Pattern recognition.

His research links Social network with Machine learning. His study in Theoretical computer science is interdisciplinary in nature, drawing from both Embedding, Graph and Graph. Recommender system is the focus of his Information retrieval research.

He most often published in these fields:

  • Data mining (29.80%)
  • Artificial intelligence (27.75%)
  • Machine learning (15.47%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (27.75%)
  • Machine learning (15.47%)
  • Graph (7.38%)

In recent papers he was focusing on the following fields of study:

Philip S. Yu focuses on Artificial intelligence, Machine learning, Graph, Theoretical computer science and Data mining. His work deals with themes such as Pattern recognition and Natural language processing, which intersect with Artificial intelligence. As a part of the same scientific study, Philip S. Yu usually deals with the Machine learning, concentrating on Training set and frequently concerns with Semi-supervised learning.

The concepts of his Graph study are interwoven with issues in Feature learning, Information retrieval and Graph. His Information retrieval research incorporates elements of Popularity, Categorization and Cluster analysis. His research investigates the connection with Data mining and areas like Pruning which intersect with concerns in Structure and Tree.

Between 2018 and 2021, his most popular works were:

  • A Comprehensive Survey on Graph Neural Networks (1287 citations)
  • Heterogeneous Graph Attention Network (291 citations)
  • Heterogeneous Information Network Embedding for Recommendation (279 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Operating system
  • The Internet

His primary areas of study are Artificial intelligence, Deep learning, Data mining, Graph and Machine learning. His biological study spans a wide range of topics, including Pattern recognition, Graph and Natural language processing. His Deep learning study also includes fields such as

  • Convolutional neural network which intersects with area such as Anomaly detection,
  • Spectral clustering, Graph embedding and Key most often made with reference to Data science.

His Data mining study incorporates themes from Scalability, Leverage, Pruning, Set and Node. His research integrates issues of Feature learning and Information retrieval in his study of Graph. His Machine learning research is multidisciplinary, relying on both Space, Feature extraction, Novelty and Hidden Markov model.

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

Top 10 algorithms in data mining

Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh.
Knowledge and Information Systems (2007)

5181 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

An effective hash-based algorithm for mining association rules

Jong Soo Park;Ming-Syan Chen;Philip S. Yu.
international conference on management of data (1995)

2491 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

Privacy-preserving data publishing: A survey of recent developments

Benjamin C. M. Fung;Ke Wang;Rui Chen;Philip S. Yu.
ACM Computing Surveys (2010)

1761 Citations

Mining concept-drifting data streams using ensemble classifiers

Haixun Wang;Wei Fan;Philip S. Yu;Jiawei Han.
knowledge discovery and data mining (2003)

1658 Citations

A holistic lexicon-based approach to opinion mining

Xiaowen Ding;Bing Liu;Philip S. Yu.
web search and data mining (2008)

1542 Citations

Outlier detection for high dimensional data

Charu C. Aggarwal;Philip S. Yu.
international conference on management of data (2001)

1455 Citations

Fast algorithms for projected clustering

Charu C. Aggarwal;Joel L. Wolf;Philip S. Yu;Cecilia Procopiuc.
international conference on management of data (1999)

1406 Citations

A General Survey of Privacy-Preserving Data Mining Models and Algorithms

Charu C. Aggarwal;Philip S. Yu.
Privacy-Preserving Data Mining (2008)

1163 Citations

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Best Scientists Citing Philip S. Yu

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 300

Tzung-Pei Hong

Tzung-Pei Hong

National University of Kaohsiung

Publications: 225

Charu C. Aggarwal

Charu C. Aggarwal

IBM (United States)

Publications: 201

Ming-Syan Chen

Ming-Syan Chen

National Taiwan University

Publications: 138

Christos Faloutsos

Christos Faloutsos

Carnegie Mellon University

Publications: 131

Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

Publications: 127

Philippe Fournier-Viger

Philippe Fournier-Viger

Harbin Institute of Technology

Publications: 117

Jeffrey Xu Yu

Jeffrey Xu Yu

Chinese University of Hong Kong

Publications: 116

Lei Chen

Lei Chen

Hong Kong University of Science and Technology

Publications: 109

Longbing Cao

Longbing Cao

University of Technology Sydney

Publications: 106

Jian Pei

Jian Pei

Simon Fraser University

Publications: 103

Huan Liu

Huan Liu

Arizona State University

Publications: 93

Carson Kai-Sang Leung

Carson Kai-Sang Leung

University of Manitoba

Publications: 90

Thomas Seidl

Thomas Seidl

Ludwig-Maximilians-Universität München

Publications: 85

Xindong Wu

Xindong Wu

Hefei University of Technology

Publications: 85

Vincent S. Tseng

Vincent S. Tseng

National Yang Ming Chiao Tung University

Publications: 83

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