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BMCBI
2007
123views more  BMCBI 2007»
15 years 6 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
ICANN
2009
Springer
15 years 4 months ago
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data
Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on...
André C. A. Nascimento, Ricardo Bastos Cava...
SDM
2012
SIAM
452views Data Mining» more  SDM 2012»
13 years 9 months ago
Density-based Projected Clustering over High Dimensional Data Streams
Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...
Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...
SIGMOD
2004
ACM
140views Database» more  SIGMOD 2004»
16 years 6 months ago
Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to c...
Corrine Cheng, Jörg Sander, Samer Nassar
ICDE
2008
IEEE
157views Database» more  ICDE 2008»
16 years 7 months ago
Approximate Clustering on Distributed Data Streams
Abstract-- We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites whi...
Qi Zhang, Jinze Liu, Wei Wang 0010