H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 52 Citations 16,420 171 World Ranking 2613 National Ranking 250

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • Machine learning

His primary areas of investigation include Data mining, Association rule learning, Knowledge extraction, Theoretical computer science and Uncertain data. His Data mining research incorporates elements of Transaction data, Spatial database, Graph and Artificial intelligence. David W. Cheung has included themes like Biological database, Probabilistic logic, Transaction processing and Database in his Association rule learning study.

The study incorporates disciplines such as Information retrieval and Data science in addition to Knowledge extraction. The Theoretical computer science study combines topics in areas such as Fuzzy electronics, Sequential algorithm, Scalability, Distributed database and Distributed algorithm. His work carried out in the field of Uncertain data brings together such families of science as Computational geometry, Probability density function, k-means clustering and Pruning.

His most cited work include:

  • SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler (3123 citations)
  • Maintenance of discovered association rules in large databases: an incremental updating technique (723 citations)
  • Secure kNN computation on encrypted databases (516 citations)

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

David W. Cheung spends much of his time researching Data mining, Association rule learning, Information retrieval, Algorithm and Artificial intelligence. His research in Data mining tackles topics such as XML which are related to areas like Tree. His Association rule learning study combines topics in areas such as Parallel algorithm, Skewness, Adaptive algorithm and Shared memory.

His Algorithm research includes elements of Theoretical computer science and Pruning. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition. His biological study deals with issues like Data science, which deal with fields such as Disparate system.

He most often published in these fields:

  • Data mining (46.92%)
  • Association rule learning (15.17%)
  • Information retrieval (14.22%)

What were the highlights of his more recent work (between 2013-2019)?

  • Data mining (46.92%)
  • Information retrieval (14.22%)
  • Data science (6.16%)

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

David W. Cheung mainly focuses on Data mining, Information retrieval, Data science, Social media and World Wide Web. His studies in Data mining integrate themes in fields like Feature, Spatial analysis and Social network. His Spatial analysis research is multidisciplinary, incorporating elements of CURE data clustering algorithm, Canopy clustering algorithm, Fuzzy clustering, Correlation clustering and Data stream clustering.

His Information retrieval study incorporates themes from Web mapping, Aggregate and Index. David W. Cheung interconnects Sentiment analysis and Knowledge extraction in the investigation of issues within Data science. His Knowledge extraction research integrates issues from Volume and Big data.

Between 2013 and 2019, his most popular works were:

  • Erratum to "SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler" [GigaScience, (2012), 1, 18] (116 citations)
  • Secure query processing with data interoperability in a cloud database environment (57 citations)
  • Density-based place clustering in geo-social networks (50 citations)

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

  • Artificial intelligence
  • Database
  • Machine learning

His main research concerns Data mining, Recommender system, Programming language, Short read and Statistics. Data mining and Multiple sequence alignment are two areas of study in which David W. Cheung engages in interdisciplinary work. The various areas that David W. Cheung examines in his Recommender system study include Test, Value and Internet privacy.

His Bayesian probability study in the realm of Statistics connects with subjects such as Group.

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.

Top Publications

SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler

Ruibang Luo;Binghang Liu;Yinlong Xie;Yinlong Xie;Zhenyu Li.
GigaScience (2012)

3602 Citations

Maintenance of discovered association rules in large databases: an incremental updating technique

D.W. Cheung;Jiawei Han;V.T. Ng;C.Y. Wong.
international conference on data engineering (1996)

1371 Citations

Secure kNN computation on encrypted databases

Wai Kit Wong;David Wai-lok Cheung;Ben Kao;Nikos Mamoulis.
international conference on management of data (2009)

814 Citations

A fast distributed algorithm for mining association rules

D.W. Cheung;Jiawei Han;V.T. Ng;A.W. Fu.
international conference on parallel and distributed information systems (1996)

715 Citations

A General Incremental Technique for Maintaining Discovered Association Rules

David Wai-Lok Cheung;Sau Dan Lee;Ben Kao.
database systems for advanced applications (1997)

643 Citations

Efficient mining of association rules in distributed databases

D.W. Cheung;V.T. Ng;A.W. Fu;Yongjian Fu.
IEEE Transactions on Knowledge and Data Engineering (1996)

566 Citations

Enhancing Effectiveness of Outlier Detections for Low Density Patterns

Jian Tang;Zhixiang Chen;Ada Wai-Chee Fu;David Wai-Lok Cheung.
knowledge discovery and data mining (2002)

505 Citations

Mining frequent spatio-temporal sequential patterns

Huiping Cao;N. Mamoulis;D.W. Cheung.
international conference on data mining (2005)

393 Citations

Mining, indexing, and querying historical spatiotemporal data

Nikos Mamoulis;Huiping Cao;George Kollios;Marios Hadjieleftheriou.
knowledge discovery and data mining (2004)

376 Citations

Uncertainty reasoning based on cloud models in controllers

D. Li;D. Cheung;Xuemei Shi;Vincent To Yee Ng.
Computers & Mathematics With Applications (1998)

374 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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