Optimal constraint-based decision tree induction from itemset lattices

In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local...

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Bibliographic Details
Published in:Data mining and knowledge discovery 2010-07, Vol.21 (1), p.9-51
Main Authors: Nijssen, Siegfried, Fromont, Elisa
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Chemistry and Earth Sciences
Computer Science
Data Mining and Knowledge Discovery
Decision trees
Heuristic
Information Storage and Retrieval
Joints
Lattices
Learning
Machine Learning
Mining
Optimization
Performance evaluation
Physics
Post-processing
State of the art
Statistics for Engineering
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Summary:In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-010-0174-x