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ICDM
2002
IEEE
158views Data Mining» more  ICDM 2002»
15 years 9 months ago
Adaptive dimension reduction for clustering high dimensional data
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst...
ICPR
2010
IEEE
15 years 2 months ago
On Dynamic Weighting of Data in Clustering with K-Alpha Means
Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a...
Sibao Chen, Haixian Wang, Bin Luo
BMCBI
2008
102views more  BMCBI 2008»
15 years 4 months ago
Response projected clustering for direct association with physiological and clinical response data
Background: Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients'...
Sung-Gon Yi, Taesung Park, Jae K. Lee
IDA
1999
Springer
15 years 9 months ago
3D Grand Tour for Multidimensional Data and Clusters
Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the...
Li Yang
TKDE
2002
168views more  TKDE 2002»
15 years 4 months ago
CLARANS: A Method for Clustering Objects for Spatial Data Mining
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contrib...
Raymond T. Ng, Jiawei Han