K
Kyuseok Shim
Researcher at Seoul National University
Publications - 179
Citations - 17827
Kyuseok Shim is an academic researcher from Seoul National University. The author has contributed to research in topics: Query optimization & Approximation algorithm. The author has an hindex of 50, co-authored 170 publications receiving 17079 citations. Previous affiliations of Kyuseok Shim include Bell Labs & Samsung.
Papers
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Proceedings ArticleDOI
CURE: an efficient clustering algorithm for large databases
TL;DR: This work proposes a new clustering algorithm called CURE that is more robust to outliers, and identifies clusters having non-spherical shapes and wide variances in size, and demonstrates that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality.
Journal ArticleDOI
Efficient algorithms for mining outliers from large data sets
TL;DR: A novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor is proposed and the top n points in this ranking are declared to be outliers.
Journal ArticleDOI
ROCK: a robust clustering algorithm for categorical attributes
TL;DR: This paper develops a robust hierarchical clustering algorithm ROCK that employs links and not distances when merging clusters, and indicates that ROCK not only generates better quality clusters than traditional algorithms, but it also exhibits good scalability properties.
Proceedings ArticleDOI
ROCK: a robust clustering algorithm for categorical attributes
TL;DR: This work develops a robust hierarchical clustering algorithm, ROCK, that employs links and not distances when merging clusters, and shows that ROCK not only generates better quality clusters than traditional algorithms, but also exhibits good scalability properties.
Journal ArticleDOI
Cure: an efficient clustering algorithm for large databases
TL;DR: It is demonstrated that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality.