C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
The common order-theoretic structure of version spaces and ATMSs
Artificial Intelligence
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Geography of Differences between Two Classes of Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Feature space transformation and decision results interpretation
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Controlling patterns of geospatial phenomena
Geoinformatica
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Describing and capturing significant differences between two classes of data is an important data mining and classification research topic. In this paper, we use emerging patterns to describe these significant differences. Such a pattern occurs in one class of samples -- its "home" class -- with a high frequency but does not exist in the other class, so it can be considered as a characteristic property of its home class. We call the collection of all such patterns a space. Beyond the space, there are patterns that occur in both of the classes or that do not occur in any of the two classes. Within the space, the most general and most specific patterns bound the other patterns in a lossless convex way. We decompose the space into a terrace of pattern plateaus based on their frequency. We use the most general patterns to construct accurate classifiers. We also use these patterns in the bio-medical domain to suggest treatment plans for adjusting the expression levels of certain genes so that patients can be cured.