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.
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.
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.
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.
SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler
Ruibang Luo;Binghang Liu;Yinlong Xie;Yinlong Xie;Zhenyu Li.
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)
Secure kNN computation on encrypted databases
Wai Kit Wong;David Wai-lok Cheung;Ben Kao;Nikos Mamoulis.
international conference on management of data (2009)
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)
A General Incremental Technique for Maintaining Discovered Association Rules
David Wai-Lok Cheung;Sau Dan Lee;Ben Kao.
database systems for advanced applications (1997)
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)
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)
Mining frequent spatio-temporal sequential patterns
Huiping Cao;N. Mamoulis;D.W. Cheung.
international conference on data mining (2005)
Mining, indexing, and querying historical spatiotemporal data
Nikos Mamoulis;Huiping Cao;George Kollios;Marios Hadjieleftheriou.
knowledge discovery and data mining (2004)
Uncertainty reasoning based on cloud models in controllers
D. Li;D. Cheung;Xuemei Shi;Vincent To Yee Ng.
Computers & Mathematics With Applications (1998)
Profile was last updated on December 6th, 2021.
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