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Yiming Ma
Researcher at National University of Singapore
Publications - 72
Citations - 7265
Yiming Ma is an academic researcher from National University of Singapore. The author has contributed to research in topics: Association rule learning & Slow light. The author has an hindex of 27, co-authored 68 publications receiving 6545 citations. Previous affiliations of Yiming Ma include Google & Zhejiang University.
Papers
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Proceedings Article
Integrating classification and association rule mining
Bing Liu,Wynne Hsu,Yiming Ma +2 more
TL;DR: The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs), and shows that the classifier built this way is more accurate than that produced by the state-of-the-art classification system C4.5.
Proceedings ArticleDOI
Scaling up all pairs similarity search
TL;DR: This work proposes a simple algorithm based on novel indexing and optimization strategies that solves the problem of finding all pairs of vectors whose similarity score is above a given threshold without relying on approximation methods or extensive parameter tuning.
Proceedings ArticleDOI
Mining association rules with multiple minimum supports
Bing Liu,Wynne Hsu,Yiming Ma +2 more
TL;DR: This paper proposes a novel technique that allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database and shows that the technique is very effective.
Proceedings ArticleDOI
Pruning and summarizing the discovered associations
Bing Liu,Wynne Hsu,Yiming Ma +2 more
TL;DR: The technique first prunes the discovered associations to remove those insignificant associations, and then finds a special subset of the unpruned associations to form a summary of the discovered association rules, which are then called the direction setting rules.
Journal ArticleDOI
Analyzing the subjective interestingness of association rules
TL;DR: This article describes how the interestingness analysis system (IAS) leverages the user's existing domain knowledge to analyze discovered associations and then rank discovered rules according to various interestingness criteria, such as conformity and various types of unexpectedness.