Her primary areas of study are Data mining, Data publishing, Nearest neighbor search, Theoretical computer science and Artificial intelligence. Her Data mining study combines topics from a wide range of disciplines, such as Metric, Cluster analysis and k-anonymity. Her biological study spans a wide range of topics, including Online transaction processing, Information sensitivity, Distributed transaction and Anonymity.
Her Nearest neighbor search study incorporates themes from Point, Nearest-neighbor chain algorithm, Search engine indexing and Pruning. In her research on the topic of Theoretical computer science, FSA-Red Algorithm and Apriori algorithm is strongly related with Scalability. Hierarchical clustering, CURE data clustering algorithm and Correlation clustering is closely connected to Pattern recognition in her research, which is encompassed under the umbrella topic of Artificial intelligence.
Data mining, Theoretical computer science, Artificial intelligence, Data publishing and Distributed computing are her primary areas of study. She has included themes like Cluster analysis and k-anonymity in her Data mining study. Her Theoretical computer science study combines topics in areas such as Scalability, Computation, Maximal independent set and Graph.
Her Artificial intelligence research includes elements of Machine learning and Pattern recognition. Her Data publishing research incorporates themes from Adversary, Information sensitivity, Information privacy and Anonymity. Her Anomaly detection research includes themes of Algorithm and Outlier.
Ada Wai-Chee Fu focuses on Theoretical computer science, Graph, Distributed computing, Computation and Data mining. Ada Wai-Chee Fu interconnects Scalability, Key and Maximal independent set in the investigation of issues within Theoretical computer science. Ada Wai-Chee Fu has researched Graph in several fields, including Algorithm, Shortest path problem and Search engine indexing.
Her research in the fields of Distributed algorithm overlaps with other disciplines such as Road networks. The concepts of her Computation study are interwoven with issues in Redundancy, Artificial intelligence, Machine learning and Enumeration. Her research in Data mining is mostly focused on Differential privacy.
Her primary areas of investigation include Theoretical computer science, Graph, Computation, Approximation algorithm and Vertex. Her Theoretical computer science research is multidisciplinary, relying on both Scalability, Pathwidth, Graph product, Maximal independent set and Topology. Her Scalability research incorporates elements of Implicit graph, Graph and Exact algorithm.
Her studies deal with areas such as Redundancy, Search engine indexing, Artificial intelligence and Enumeration as well as Computation. The study incorporates disciplines such as Keyword search, Keyword density, RDF and Data mining in addition to Approximation algorithm. Her work carried out in the field of Vertex brings together such families of science as Similarity, Key, Modular decomposition and Indifference graph.
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.
Efficient time series matching by wavelets
Kin-Pong Chan;Ada Wai-Chee Fu.
international conference on data engineering (1999)
HOT SAX: efficiently finding the most unusual time series subsequence
E. Keogh;J. Lin;A. Fu.
international conference on data mining (2005)
Entropy-based subspace clustering for mining numerical data
Chun-Hung Cheng;Ada Waichee Fu;Yi Zhang.
knowledge discovery and data mining (1999)
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)
Mining association rules with weighted items
C.H. Cai;A.W.C. Fu;C.H. Cheng;W.W. Kwong.
international database engineering and applications symposium (1998)
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)
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Raymond Chi-Wing Wong;Jiuyong Li;Ada Wai-Chee Fu;Ke Wang.
knowledge discovery and data mining (2006)
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)
Utility-based anonymization using local recoding
Jian Xu;Wei Wang;Jian Pei;Xiaoyuan Wang.
knowledge discovery and data mining (2006)
K-isomorphism: privacy preserving network publication against structural attacks
James Cheng;Ada Wai-chee Fu;Jia Liu.
international conference on management of data (2010)
Profile was last updated on December 6th, 2021.
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