Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Transactions on rough sets XII
A framework to mine high-level emerging patterns by attribute-oriented induction
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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Emerging Patterns (EPs) are a data mining model that is useful as a means of discovering distinctions inherently present amongst a collection of datasets. However, current EP mining algorithms do not handle attributes whose values are asscociated with taxonomies (is-a hierarchies). Current EP mining techniques are restricted to using only the leaf-level attribute-values in a taxonomy. In this paper, we formally introduce the problem of mining generalised emerging patterns. Given a large data set, where some attributes are hierarchical, we find emerging patterns that consist of items at any level of the taxonomies. Generalised EPs are more concise and interpretable when used to describe some distinctive characteristics of a class of data. They are also considered to be more expressive because they include items at higher levels of the hierarchies, which have larger supports than items at the leaf level. We formulate the problem of mining generalised EPs, and present an algorithm for this task. We demonstrate that the discovered generalised patterns, which contain items at higher levels in the hierarchies, have greater support than traditional leaf-level EPs according to our experimental results based on ten benchmark datasets.