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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
IBM
Research output
:
Contribution to conference
›
Paper
›
peer-review
168
Scopus citations
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Dive into the research topics of 'A local search approximation algorithm for k-means clustering'. Together they form a unique fingerprint.
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Keyphrases
Approximation Algorithms
100%
K-means
100%
Local Search
100%
Approximation Factor
66%
Tight
33%
Heuristic-based
33%
Efficient Approximation Algorithm
33%
Mean Square Distance
33%
K-points
33%
Lloyd Algorithm
33%
Exact Polynomial-time Algorithm
33%
Improvement Heuristics
33%
Asymptotic Efficiency
33%
Local Improvement
33%
Mathematics
Data Point
100%
Local Search
100%
Constant Factor
50%
Integer
50%
Minimizes
50%
Polynomial Time
50%
Dimensional Space
50%
Computer Science
Approximation Algorithms
100%
k-means Clustering
100%
approximation factor
50%
polynomial-time algorithm
25%
Squared Distance
25%
Constant Factor
25%
Dimensional Space
25%
Lloyd Algorithm
25%