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Ada Wai-Chee Fu
Researcher at The Chinese University of Hong Kong
Publications - 170
Citations - 12857
Ada Wai-Chee Fu is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Data publishing & Information privacy. The author has an hindex of 55, co-authored 168 publications receiving 12054 citations. Previous affiliations of Ada Wai-Chee Fu include University of Hong Kong & Wilfrid Laurier University.
Papers
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Proceedings ArticleDOI
Efficient time series matching by wavelets
Kin-Pong Chan,Ada Wai-Chee Fu +1 more
TL;DR: This paper proposes to use Haar Wavelet Transform for time series indexing and shows that Haar transform can outperform DFT through experiments, and proposes a two-phase method for efficient n-nearest neighbor query in time series databases.
Proceedings ArticleDOI
HOT SAX: efficiently finding the most unusual time series subsequence
TL;DR: The utility of discords with objective experiments on domains as diverse as Space Shuttle telemetry monitoring, medicine, surveillance, and industry, and the effectiveness of the discord discovery algorithm with more than one million experiments, on 82 different datasets from diverse domains are demonstrated.
Proceedings ArticleDOI
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
TL;DR: It is proved that the optimal (α, k)-anonymity problem is NP-hard, and a local-recoding algorithm is proposed which is more scalable and result in less data distortion.
Proceedings ArticleDOI
Entropy-based subspace clustering for mining numerical data
TL;DR: This work considers a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector, and identifies new meaningful criteria of high density and correlation of dimensions for goodness of clustering in subspaces.
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.