His primary scientific interests are in Data mining, Theoretical computer science, Graph, Artificial intelligence and Graph database. Wilfred Ng is interested in Data stream mining, which is a branch of Data mining. His Theoretical computer science research incorporates themes from Locality-sensitive hashing, Hash function, Vertex and Partition.
His Graph study combines topics from a wide range of disciplines, such as Load balancing and Programming paradigm. His work carried out in the field of Artificial intelligence brings together such families of science as Optimization problem, Machine learning, Concept mining and Set. His Graph database research includes themes of Efficient algorithm and Correlation coefficient.
Data mining, Information retrieval, Theoretical computer science, Database and Artificial intelligence are his primary areas of study. His work on Data stream mining as part of general Data mining study is frequently connected to Data stream, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Information retrieval research is multidisciplinary, incorporating perspectives in XML, Efficient XML Interchange and XML database.
His research integrates issues of Graph, Database design, Functional dependency, Relational database and Graph in his study of Theoretical computer science. His research in Artificial intelligence intersects with topics in Machine learning and Natural language processing. His biological study deals with issues like Topic model, which deal with fields such as World Wide Web.
His primary areas of investigation include Artificial intelligence, Theoretical computer science, Machine learning, Data mining and Information retrieval. His Artificial intelligence research is multidisciplinary, relying on both Algorithm design and Natural language processing. His Theoretical computer science study combines topics in areas such as Vertex, Graph, Hash function and Programming paradigm.
His Machine learning research is multidisciplinary, incorporating elements of Question answering and User Friendly. The study incorporates disciplines such as Set, Feature selection, Document clustering and Pruning in addition to Data mining. His Information retrieval research integrates issues from Crowdsourcing and World Wide Web.
His primary areas of study are Question answering, Theoretical computer science, Artificial intelligence, Machine learning and Graph. His studies in Question answering integrate themes in fields like Sentiment analysis, Database, Inference, Crowdsourcing and Data science. His studies deal with areas such as Programming paradigm, Vertex, Graph, Nearest neighbor search and Partition as well as Theoretical computer science.
The concepts of his Artificial intelligence study are interwoven with issues in Recommender system and Collaborative filtering. His research integrates issues of Session and Identification in his study of Machine learning. His Graph study also includes fields such as
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Fg-index: towards verification-free query processing on graph databases
James Cheng;Yiping Ke;Wilfred Ng;An Lu.
international conference on management of data (2007)
Blogel: a block-centric framework for distributed computation on real-world graphs
Da Yan;James Cheng;Yi Lu;Wilfred Ng.
very large data bases (2014)
Personalized Concept-Based Clustering of Search Engine Queries
K.W.-T. Leung;W. Ng;Dik Lun Lee.
IEEE Transactions on Knowledge and Data Engineering (2008)
Locality-sensitive hashing scheme based on dynamic collision counting
Junhao Gan;Jianlin Feng;Qiong Fang;Wilfred Ng.
international conference on management of data (2012)
A survey on algorithms for mining frequent itemsets over data streams
James Cheng;Yiping Ke;Wilfred Ng.
Knowledge and Information Systems (2008)
Expert Finding for Question Answering via Graph Regularized Matrix Completion
Zhou Zhao;Lijun Zhang;Xiaofei He;Wilfred Ng.
IEEE Transactions on Knowledge and Data Engineering (2015)
Xqzip: Querying compressed XML using structural indexing
James Cheng;Wilfred Ng.
Lecture Notes in Computer Science (2004)
Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation
Da Yan;James Cheng;Yi Lu;Wilfred Ng.
the web conference (2015)
Vague sets or intuitionistic fuzzy sets for handling vague data: Which one is better?
An Lu;Wilfred Ng.
Lecture Notes in Computer Science (2005)
Query-aware locality-sensitive hashing for approximate nearest neighbor search
Qiang Huang;Jianlin Feng;Yikai Zhang;Qiong Fang.
very large data bases (2015)
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