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» Fast mining of spatial collocations
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KDD
2004
ACM
157views Data Mining» more  KDD 2004»
13 years 10 months ago
Fast mining of spatial collocations
Spatial collocation patterns associate the co-existence of nonspatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs ...
Xin Zhang, Nikos Mamoulis, David W. Cheung, Yutao ...
CVPR
2007
IEEE
14 years 7 months ago
Discovery of Collocation Patterns: from Visual Words to Visual Phrases
A visual word lexicon can be constructed by clustering primitive visual features, and a visual object can be described by a set of visual words. Such a "bag-of-words" re...
Junsong Yuan, Ying Wu, Ming Yang
ICDM
2007
IEEE
105views Data Mining» more  ICDM 2007»
13 years 11 months ago
Fast Mining of Complex Spatial Co-location Patterns Using GLIMIT
Most algorithms for mining interesting spatial colocations integrate the co-location / clique generation task with the interesting pattern mining task, and are usually based on th...
Florian Verhein, Ghazi Al-Naymat
KDD
2009
ACM
151views Data Mining» more  KDD 2009»
14 years 5 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...
ICDM
2003
IEEE
138views Data Mining» more  ICDM 2003»
13 years 10 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...