K
King-Ip Lin
Researcher at University of Memphis
Publications - 45
Citations - 4338
King-Ip Lin is an academic researcher from University of Memphis. The author has contributed to research in topics: Nearest neighbor search & Cluster analysis. The author has an hindex of 19, co-authored 44 publications receiving 4266 citations. Previous affiliations of King-Ip Lin include IBM & University of Maryland, College Park.
Papers
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Proceedings ArticleDOI
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
Christos Faloutsos,King-Ip Lin +1 more
TL;DR: A fast algorithm to map objects into points in some k-dimensional space (k is user-defined), such that the dis-similarities are preserved, and this method is introduced from pattern recognition, namely, Multi-Dimensional Scaling (MDS).
Proceedings Article
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
TL;DR: A new model of similarity of time sequences is introduced that captures the intuitive notion that two sequences should be considered similar if they have enough non-overlapping time-ordered pairs of subsequences thar are similar.
Proceedings Article
Rule discovery from time series
TL;DR: In this article, the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series, was considered, and adaptive methods for finding rules of the above type from time-series data were described.
Journal ArticleDOI
The TV-tree: an index structure for high-dimensional data
TL;DR: A file structure to index high-dimensionality data, which are typically points in some feature space, and the design of the tree structure and the associated algorithms that handle such “varying length” feature vectors are presented.
Proceedings ArticleDOI
An index structure for efficient reverse nearest neighbor queries
Congyun Yang,King-Ip Lin +1 more
TL;DR: A new index structure is introduced, the Rdnn-tree, that answers both RNN and NN queries efficiently and outperforms existing methods in various aspects, and makes the index structure extremely preferable in both static and dynamic cases.