Aggregated Subset Mining

Authors:
Albrecht Zimmermann;Björn Bringmann
Affiliations:
Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium 3001;Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium 3001
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

Citing 6
Cited 0

Bagging predictors

Machine Learning
Making large-scale support vector machine learning practical

Advances in kernel methods
Mining Free Itemsets under Constraints

IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity

Bioinformatics
The Chosen Few: On Identifying Valuable Patterns

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Don't be afraid of simpler patterns

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

The usual data mining setting uses the full amount of data to derive patterns for different purposes. Taking cues from machine learning techniques, we explore ways to divide the data into subsets, mine patterns on them and use post-processing techniques for acquiring the result set. Using the patterns as features for a classification task to evaluate their quality, we compare the different subset compositions, and selection techniques. The two main results --- that small independent sets are better suited than large amounts of data, and that uninformed selection techniques perform well --- can to a certain degree be explained by quantitative characteristics of the derived pattern sets.