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SDM
2004
SIAM
194views Data Mining» more  SDM 2004»
15 years 8 months ago
Finding Frequent Patterns in a Large Sparse Graph
Graph-based modeling has emerged as a powerful abstraction capable of capturing in a single and unified framework many of the relational, spatial, topological, and other characteri...
Michihiro Kuramochi, George Karypis
DMIN
2006
132views Data Mining» more  DMIN 2006»
15 years 8 months ago
Discovering Accurate and Interesting Classification Rules Using Genetic Algorithm
Discovering accurate and interesting classification rules is a significant task in the post-processing stage of a data mining (DM) process. Therefore, an optimization problem exis...
Janaki Gopalan, Reda Alhajj, Ken Barker
MLDM
2009
Springer
16 years 1 months ago
PMCRI: A Parallel Modular Classification Rule Induction Framework
In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction...
Frederic T. Stahl, Max A. Bramer, Mo Adda
KDD
1998
ACM
120views Data Mining» more  KDD 1998»
15 years 11 months ago
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution
This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
Tim Oates, David Jensen
KDD
2002
ACM
106views Data Mining» more  KDD 2002»
16 years 7 months ago
Selecting the right interestingness measure for association patterns
Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as...
Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava