Efficient Mining of Non-derivable Emerging Patterns
Emerging pattern mining is an important data mining task for various decision making. However, it often presents a large number of emerging patterns, most of which are not useful as their emergence are derivable from other emerging patterns. Such derivable emerging patterns most often are trivial in decision making. To enable mine the set of non-derivable emerging patterns for decision making, we employ deduction rules in identifying the set of non-derivable emerging patterns. We subsequently make use of a significance test to identify the set of significant non-derivable emerging patterns. Finally, we develop NEPs, an efficient framework for mining the set of non-derivable and significant non-derivable emerging patterns. Experimentally, NEPs is efficient, and the non-derivable emerging pattern set which is smaller than the set of all emerging patterns, shows potentials in trend prediction on a Twitter dataset.
KeywordsFrequent patterns Emerging patterns Non-derivable emerging patterns Significance test
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