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
Making Use of the Most Expressive Jumping Emerging Patterns for Classification
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Diverging patterns: discovering significant frequency change dissimilarities in large databases
Proceedings of the 18th ACM conference on Information and knowledge management
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
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Finding relational expressions which exist frequently in one class of data while not in the other class of data is an interesting work. In this paper, a relational expression of this kind is defined as a contrast inequality. Gene Expression Programming (GEP) is powerful to discover relations from data and express them in mathematical level. Hence, it is desirable to apply GEP to such mining task. The main contributions of this paper include: (1) introducing the concept of contrast inequality mining, (2) designing a two-genome chromosome structure to guarantee that each individual in GEP is a valid inequality, (3) proposing a new genetic mutation to improve the efficiency of evolving contrast inequalities, (4) presenting a GEP-based method to discover contrast inequalities, (5) giving an extensive performance study on real-world datasets. The experimental results show that the proposed methods are effective. Contrast inequalities with high discriminative power are discovered from the real-world datasets. Some potential works on contrast inequality mining are discussed.