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ICALP
2005
Springer
14 years 1 months ago
Linear Time Algorithms for Clustering Problems in Any Dimensions
Abstract. We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certa...
Amit Kumar, Yogish Sabharwal, Sandeep Sen
ICDE
2007
IEEE
211views Database» more  ICDE 2007»
14 years 2 months ago
Document Representation and Dimension Reduction for Text Clustering
Increasingly large text datasets and the high dimensionality associated with natural language create a great challenge in text mining. In this research, a systematic study is cond...
M. Mahdi Shafiei, Singer Wang, Roger Zhang, Evange...
ICCV
2003
IEEE
14 years 10 months ago
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are co...
Bogdan Georgescu, Ilan Shimshoni, Peter Meer
RTSS
2006
IEEE
14 years 2 months ago
Modeling and Worst-Case Dimensioning of Cluster-Tree Wireless Sensor Networks
Time-sensitive Wireless Sensor Network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worst-case...
Anis Koubaa, Mário Alves, Eduardo Tovar
KDD
2000
ACM
96views Data Mining» more  KDD 2000»
13 years 12 months ago
Using the fractal dimension to cluster datasets
Daniel Barbará, Ping Chen