R
Ruth Silverman
Researcher at University of Maryland, College Park
Publications - 21
Citations - 9879
Ruth Silverman is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Approximation algorithm & Canopy clustering algorithm. The author has an hindex of 14, co-authored 21 publications receiving 9177 citations. Previous affiliations of Ruth Silverman include University of the District of Columbia.
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Journal ArticleDOI
An efficient k-means clustering algorithm: analysis and implementation
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Journal ArticleDOI
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
TL;DR: In this paper, it was shown that given an integer k ≥ 1, (1 + ϵ)-approximation to the k nearest neighbors of q can be computed in additional O(kd log n) time.
Proceedings ArticleDOI
A local search approximation algorithm for k-means clustering
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work considers the question of whether there exists a simple and practical approximation algorithm for k-means clustering, and presents a local improvement heuristic based on swapping centers in and out that yields a (9+ε)-approximation algorithm.
Proceedings ArticleDOI
An optimal algorithm for approximate nearest neighbor searching
TL;DR: It is shown that it is possible to preprocess a set of data points in real D-dimensional space in O(kd) time and in additional space, so that given a query point q, the closest point of S to S to q can be reported quickly.
Proceedings ArticleDOI
The analysis of a simple k-means clustering algorithm
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This paper presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it differs from most other approaches in that it precomputes a kd-tree data structure for the data points rather than the center points.