C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The common order-theoretic structure of version spaces and ATMSs
Artificial Intelligence
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Machine Learning
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Instance-Based Classification by Emerging Patterns
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Version spaces: a candidate elimination approach to rule learning
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
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In this work, we review an important kind of knowledge pattern, emerging patterns (EPs). Emerging patterns are associated with two data sets, and can be used to describe significant changes between the two data sets. To discover all EPs embedded in high-dimension and large-volume databases is a challenging problem due to the number of candidates. We describe a special type of EP, called jumping emerging patterns (JEPs) and review some properties of JEP spaces (the spaces of jumping emerging patterns). We describe efficient border-based algorithms to derive the boundary elements of JEP spaces. Moreover, we describe a new classifier, called DeEPs, which makes use of the discriminating power of emerging patterns. The experimental results show that the accuracy of DeEPs is much better than that of k-nearest neighbor and that of C5.0.