Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
Attribute-oriented induction in data mining
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Levelwise Search and Borders of Theories in KnowledgeDiscovery
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
IEEE Transactions on Knowledge and Data Engineering
A probabilistic classifier system and its application in data mining
Evolutionary Computation
World Wide Web
Efficient incremental mining of contrast patterns in changing data
Information Processing Letters
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Transactions on rough sets XII
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Emerging patterns (EPs) are useful knowledge patterns with many applications. In recent studies on bio-medical profiling data, we have successfully used such patterns to solve difficult cancer diagnosis problems and produced higher classification accuracy when compared to alternative methods. However, the discovery of EPs is a challenging and computationally expensive problem.In this paper, we study how to incrementally modify and maintain the concise boundary descriptions of the space of all emerging patterns when small changes occur to the data. As EP spaces are convex, the maintenance on the bounds guarantees that no desired patterns are lost. We introduce algorithms to handle four types of changes: insertion of new data, deletion of old data, addition of new attributes, and deletion of old attributes. We compare these incremental algorithms, on six benchmark data sets, against an efficient algorithm that computes from scratch. The results show that the incremental algorithms are much faster than the From-Scratch method, often with tremendous speed-up rates.