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» Topographic Mapping of Large Dissimilarity Data Sets
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DMKD
1997
ACM
308views Data Mining» more  DMKD 1997»
13 years 11 months ago
A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation becau...
Zhexue Huang
ICDM
2003
IEEE
138views Data Mining» more  ICDM 2003»
14 years 22 days ago
PixelMaps: A New Visual Data Mining Approach for Analyzing Large Spatial Data Sets
PixelMaps are a new pixel-oriented visual data mining technique for large spatial datasets. They combine kerneldensity-based clustering with pixel-oriented displays to emphasize c...
Daniel A. Keim, Christian Panse, Mike Sips, Stephe...
HPDC
2010
IEEE
13 years 8 months ago
Browsing large scale cheminformatics data with dimension reduction
Visualization of large-scale high dimensional data tool is highly valuable for scientific discovery in many fields. We present PubChemBrowse, a customized visualization tool for c...
Jong Youl Choi, Seung-Hee Bae, Judy Qiu, Geoffrey ...
JMLR
2010
230views more  JMLR 2010»
13 years 2 months ago
Learning Dissimilarities for Categorical Symbols
In this paper we learn a dissimilarity measure for categorical data, for effective classification of the data points. Each categorical feature (with values taken from a finite set...
Jierui Xie, Boleslaw K. Szymanski, Mohammed J. Zak...
BMCBI
2011
12 years 11 months ago
PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus ge
Background: Results of phylogenetic analysis are often visualized as phylogenetic trees. Such a tree can typically only include up to a few hundred sequences. When more than a few...
Jiajie Zhang, Amir Madany Mamlouk, Thomas Martinet...