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NPL
2002
151views more  NPL 2002»
13 years 4 months ago
Additive Composition of Supervised Self Organizing Maps
The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined by prior knowledge of the problem that permits to split...
Jean-Luc Buessler, Jean-Philippe Urban, Julien Gre...
AIR
2006
107views more  AIR 2006»
13 years 4 months ago
Just enough learning (of association rules): the TAR2 "Treatment" learner
Abstract. An over-zealous machine learner can automatically generate large, intricate, theories which can be hard to understand. However, such intricate learning is not necessary i...
Tim Menzies, Ying Hu
COMPSAC
2004
IEEE
13 years 8 months ago
Mining Complex Relationships in the SDSS SkyServer Spatial Database
In this paper we describe the process of mining complex relationships in spatial databases using the Maximal Participation Index (maxPI), which has a property of discovering low s...
Bavani Arunasalam, Sanjay Chawla, Pei Sun, Robert ...
ICDM
2003
IEEE
123views Data Mining» more  ICDM 2003»
13 years 9 months ago
Complex Spatial Relationships
This paper describes the need for mining complex relationships in spatial data. Complex relationships are defined as those involving two or more of: multi-feature co-location, sel...
Robert Munro, Sanjay Chawla, Pei Sun
ICCS
2005
Springer
13 years 10 months ago
Morphisms in Context
Abstract. Morphisms constitute a general tool for modelling complex relationships between mathematical objects in a disciplined fashion. In Formal Concept Analysis (FCA), morphisms...
Markus Krötzsch, Pascal Hitzler, Guo-Qiang Zh...
ICDM
2007
IEEE
105views Data Mining» more  ICDM 2007»
13 years 10 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