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.
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