An efficient k-means clustering algorithm: Analysis and implementation
In k-means clustering, we are given a set of n data points in d-dimensional space R/sup
d/and an integer k and the problem is to determine a set of k points in Rd, called centers, so …
d/and an integer k and the problem is to determine a set of k points in Rd, called centers, so …
[图书][B] Combinatorial optimization
BH Korte, J Vygen, B Korte, J Vygen - 2011 - Springer
Let us start with two examples. A company has a machine which drills holes into printed
circuit boards. Since it produces many of these boards it wants the machine to complete one …
circuit boards. Since it produces many of these boards it wants the machine to complete one …
A similarity measure for text classification and clustering
YS Lin, JY Jiang, SJ Lee - IEEE transactions on knowledge and …, 2013 - ieeexplore.ieee.org
Measuring the similarity between documents is an important operation in the text processing
field. In this paper, a new similarity measure is proposed. To compute the similarity between …
field. In this paper, a new similarity measure is proposed. To compute the similarity between …
On coresets for k-means and k-median clustering
S Har-Peled, S Mazumdar - Proceedings of the thirty-sixth annual ACM …, 2004 - dl.acm.org
In this paper, we show the existence of small coresets for the problems of computing k-
median and k-means clustering for points in low dimension. In other words, we show that …
median and k-means clustering for points in low dimension. In other words, we show that …
A local search approximation algorithm for k-means clustering
In k-means clustering we are given a set of n data points in d-dimensional space ℜd and an
integer k, and the problem is to determine a set of k points in ℜd, called centers, to minimize …
integer k, and the problem is to determine a set of k points in ℜd, called centers, to minimize …
Clustering data streams: Theory and practice
The data stream model has recently attracted attention for its applicability to numerous types
of data, including telephone records, Web documents, and clickstreams. For analysis of such …
of data, including telephone records, Web documents, and clickstreams. For analysis of such …
Clustering data streams
Clustering is a useful and ubiquitous tool in data analysis. Broadly speaking, clustering is
the problem of grouping a data set into several groups such that, under some definition of …
the problem of grouping a data set into several groups such that, under some definition of …
[图书][B] Geometric approximation algorithms
S Har-Peled - 2011 - books.google.com
Exact algorithms for dealing with geometric objects are complicated, hard to implement in
practice, and slow. Over the last 20 years a theory of geometric approximation algorithms …
practice, and slow. Over the last 20 years a theory of geometric approximation algorithms …
[PDF][PDF] A survey on threshold based segmentation technique in image processing
K Bhargavi, S Jyothi - International Journal of Innovative Research …, 2014 - researchgate.net
The present paper describes the study of the threshold techniques in image segmentation.
Image segmentation is one of the fundamental approaches of the digital image processing …
Image segmentation is one of the fundamental approaches of the digital image processing …
Solution methods for the p‐median problem: An annotated bibliography
J Reese - NETWORKS: an international Journal, 2006 - Wiley Online Library
The p‐median problem is a network problem that was originally designed for, and has been
extensively applied to, facility location. In this bibliography, we summarize the literature on …
extensively applied to, facility location. In this bibliography, we summarize the literature on …