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» Topographic Mapping of Large Dissimilarity Data Sets
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DMKD
1997
ACM
308views Data Mining» more  DMKD 1997»
15 years 1 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»
15 years 2 months 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
14 years 10 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»
14 years 4 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
14 years 1 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...