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KDD
2009
ACM
151views Data Mining» more  KDD 2009»
14 years 8 months ago
A LRT framework for fast spatial anomaly detection
Given a spatial data set placed on an n ? n grid, our goal is to find the rectangular regions within which subsets of the data set exhibit anomalous behavior. We develop algorithm...
Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ran...
JGTOOLS
2008
126views more  JGTOOLS 2008»
13 years 7 months ago
Simple Empty-Space Removal for Interactive Volume Rendering
Interactive volume rendering methods such as texture-based slicing techniques and ray-casting have been well developed in recent years. The rendering performance is generally restr...
Vincent Vidal 0002, Xing Mei, Philippe Decaudin
ICDM
2003
IEEE
138views Data Mining» more  ICDM 2003»
14 years 28 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...
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
SIGKDD
2000
237views more  SIGKDD 2000»
13 years 7 months ago
The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation
Advances in data collection and storage have allowed organizations to create massive, complex and heterogeneous databases, which have stymied traditional methods of data analysis....
Stephen D. Bay, Dennis F. Kibler, Michael J. Pazza...