• Publications
  • Influence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
TLDR
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.
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
TLDR
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.
A local search approximation algorithm for k-means clustering
TLDR
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.
The analysis of a simple k-means clustering algorithm
TLDR
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.
On the Area of Overlap of Translated Polygons
TLDR
A number of mathematical results regarding the area-of-overlap function of PandQ, which has a number of applications in areas such as motion planning and object recognition, are presented.
Embedding of tree networks into hypercubes
  • A. Wu
  • Computer Science
    J. Parallel Distributed Comput.
  • 1 August 1985
A Medial Axis Transformation for Grayscale Pictures
TLDR
A generalization of the MAT in which a score is computed for each point P of a grayscale picture based on the gradient magnitudes at pairs of points that have P as their midpoint, which defines a MAT-like ``skeleton,'' which the authors may call the GRADMAT.
On the Least Trimmed Squares Estimator
TLDR
New algorithms, both exact and approximate, for computing the linear least trimmed squares estimator are presented and hardness results for exactly and approximate LTS are presented.
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