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ICTAI
2002
IEEE
10 years 4 months ago
Data Mining for Selective Visualization of Large Spatial Datasets
Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for patte...
Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, Ruli...
ICDM
2003
IEEE
138views Data Mining» more  ICDM 2003»
10 years 5 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...
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
10 years 5 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi
BMCBI
2008
114views more  BMCBI 2008»
9 years 12 months ago
Visualization of large influenza virus sequence datasets using adaptively aggregated trees with sampling-based subscale represen
Background: With the amount of influenza genome sequence data growing rapidly, researchers need machine assistance in selecting datasets and exploring the data. Enhanced visualiza...
Leonid Zaslavsky, Yiming Bao, Tatiana A. Tatusova
AI
2005
Springer
10 years 5 months ago
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets
Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we ...
Xin Wang, Howard J. Hamilton
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