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ICALP
2005
Springer
15 years 3 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»
15 years 3 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
15 years 11 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
15 years 3 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»
15 years 1 months ago
Using the fractal dimension to cluster datasets
Daniel Barbará, Ping Chen