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» Mining unexpected multidimensional rules
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JDWM
2006
84views more  JDWM 2006»
13 years 5 months ago
Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data
This paper focuses on the discovery of surprising, unexpected patterns, based on a data mining method that consists of detecting instances of Simpson's paradox. By its very n...
Carem C. Fabris, Alex Alves Freitas
KDD
1997
ACM
208views Data Mining» more  KDD 1997»
13 years 9 months ago
Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes
In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-gu...
Micheline Kamber, Jiawei Han, Jenny Chiang
KDD
2005
ACM
103views Data Mining» more  KDD 2005»
14 years 5 months ago
Fast discovery of unexpected patterns in data, relative to a Bayesian network
We consider a model in which background knowledge on a given domain of interest is available in terms of a Bayesian network, in addition to a large database. The mining problem is...
Szymon Jaroszewicz, Tobias Scheffer
IADIS
2008
13 years 6 months ago
Data Mining In Non-Stationary Multidimensional Time Series Using A Rule Similarity Measure
Time series analysis is a wide area of knowledge that studies processes in their evolution. The classical research in the area tends to find global laws underlying the behaviour o...
Nikolay V. Filipenkov
AMT
2006
Springer
91views Multimedia» more  AMT 2006»
13 years 9 months ago
Rough Set Model for Constraint-based Multi-dimensional Association Rule Mining
Abstract. This paper presents a rough set model for constraint-based multidimensional association rule mining. It first overviews the progress in constraintbased multi-dimensional ...
Wanzhong Yang, Yuefeng Li, Yue Xu, Hang Liu