Keith C. C. Chan mainly focuses on Artificial intelligence, Data mining, Machine learning, Fuzzy logic and Association rule learning. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Cognitive computing and Pattern recognition. His Data mining research incorporates themes from Algorithm, Representation, Missing data and Cluster analysis.
His Machine learning research is multidisciplinary, incorporating elements of Quality, Probabilistic logic and Set. His Fuzzy logic research integrates issues from Measure, Computer network and Sensor fusion. Keith C. C. Chan has included themes like Defuzzification, Fuzzy classification and Database design in his Association rule learning study.
Keith C. C. Chan mostly deals with Artificial intelligence, Data mining, Machine learning, Cluster analysis and Fuzzy logic. In his research on the topic of Artificial intelligence, Protein sequencing is strongly related with Pattern recognition. His research in Data mining tackles topics such as Gene which are related to areas like Computational biology.
His Machine learning research includes elements of Representation, Expert system and Knowledge acquisition. Fuzzy logic is a component of his Fuzzy set, Fuzzy set operations, Fuzzy classification and Fuzzy control system studies. His study looks at the intersection of Fuzzy classification and topics like Defuzzification with Neuro-fuzzy and Fuzzy associative matrix.
His primary areas of study are Artificial intelligence, Data mining, Machine learning, Cluster analysis and Computational biology. His biological study deals with issues like Pattern recognition, which deal with fields such as Protein sequencing. His work carried out in the field of Data mining brings together such families of science as Fuzzy set, Statistical classification, Set and Time series.
His study on Artificial neural network is often connected to Set as part of broader study in Machine learning. His Cluster analysis study combines topics from a wide range of disciplines, such as Node, Network topology, Theoretical computer science and Pairwise comparison. Keith C. C. Chan has included themes like Quantitative trait locus, microRNA, Gene and Similarity in his Computational biology study.
His primary areas of investigation include Artificial intelligence, Data mining, Protein sequencing, Cluster analysis and Computational biology. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Chen and Scale and other disciplines. His studies deal with areas such as Sentiment analysis and Social media, Social media mining as well as Data mining.
His Protein sequencing study incorporates themes from Classifier, Support vector machine, Pattern recognition and Protein–protein interaction. His Cluster analysis research incorporates elements of Disjoint sets, Algorithm design, Fuzzy logic and Power graph analysis. The study incorporates disciplines such as Cross-validation, Transcription, microRNA, Gene and Bipartite graph in addition to Computational biology.
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A novel evolutionary data mining algorithm with applications to churn prediction
Wai-Ho Au;K.C.C. Chan;Xin Yao.
IEEE Transactions on Evolutionary Computation (2003)
Class-dependent discretization for inductive learning from continuous and mixed-mode data
J.Y. Ching;A.K.C. Wong;K.C.C. Chan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1995)
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
Wai-Ho Au;Keith C. C. Chan;Andrew K. C. Wong;Yang Wang.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2005)
Mining fuzzy association rules
Keith C. C. Chan;Wai-Ho Au.
conference on information and knowledge management (1997)
Costs and benefits of ISO 9000 series: a practical study
Hareton K.N. Leung;Keith C.C. Chan;T.Y. Lee.
International Journal of Quality & Reliability Management (1999)
An effective algorithm for discovering fuzzy rules in relational databases
Wai-Ho Au;K.C.C. Chan.
ieee international conference on fuzzy systems (1998)
Pair programming productivity: Novice-novice vs. expert-expert
Kim Man Lui;Keith C. C. Chan.
International Journal of Human-computer Studies / International Journal of Man-machine Studies (2006)
Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding
Yu-An Huang;Zhu-Hong You;Xing Chen;Keith C. C. Chan.
BMC Bioinformatics (2016)
FARM: a data mining system for discovering fuzzy association rules
Wai-Ho Au;K.C.C. Chan.
ieee international conference on fuzzy systems (1999)
An evolutionary clustering algorithm for gene expression microarray data analysis
P.C.H. Ma;K.C.C. Chan;Xin Yao;D.K.Y. Chiu.
IEEE Transactions on Evolutionary Computation (2006)
Profile was last updated on December 6th, 2021.
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