D
David W. Cheung
Researcher at University of Hong Kong
Publications - 201
Citations - 14948
David W. Cheung is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Association rule learning & Cluster analysis. The author has an hindex of 48, co-authored 201 publications receiving 13625 citations. Previous affiliations of David W. Cheung include IEEE Computer Society & Hong Kong Polytechnic University.
Papers
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Journal ArticleDOI
SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler
Ruibang Luo,Binghang Liu,Yinlong Xie,Yinlong Xie,Zhenyu Li,Weihua Huang,Jianying Yuan,Guangzhu He,Yanxiang Chen,Qi Pan,Yunjie Liu,Jingbo Tang,Gengxiong Wu,Hao Zhang,Yujian Shi,Yong Liu,Chang Yu,Bo Wang,Yao Lu,Changlei Han,David W. Cheung,Siu-Ming Yiu,Shaoliang Peng,Zhu Xiao-qian,Guangming Liu,Xiangke Liao,Yingrui Li,Huanming Yang,Jian Wang,Tak-Wah Lam,Jun Wang +30 more
TL;DR: This work provides an updated assembly version of the 2008 Asian genome using SOAPdenovo2, a new algorithm design that reduces memory consumption in graph construction, resolves more repeat regions in contig assembly, increases coverage and length in scaffold construction, improves gap closing, and optimizes for large genome.
Proceedings ArticleDOI
Maintenance of discovered association rules in large databases: an incremental updating technique
TL;DR: An incremental updating technique is developed for maintenance of the association rules discovered by database mining when new transaction data are added to a transaction database.
Proceedings ArticleDOI
Secure kNN computation on encrypted databases
TL;DR: A new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product and is shown to resist practical attacks of a different background knowledge level, at a different overhead cost.
Book ChapterDOI
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
TL;DR: A connectivity-based outlier factor (COF) scheme is introduced that improves the effectiveness of an existing local outlier factors (LOF) scheme when a pattern itself has similar neighbourhood density as an outlier.
Proceedings ArticleDOI
A fast distributed algorithm for mining association rules
TL;DR: In this article, a fast distributed mining of association rules (FDM) algorithm is proposed to generate a small number of candidate sets and substantially reduce the number of messages to be passed at mining association rules.