Mining Productive Emerging Patterns and Their Application in Trend Prediction

Nofong, V.M.

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 due to random occurrence of items. Such emerging patterns would most often be detrimental in decision making where inherent relationships between the items of emerging patterns are relevant. Additionally, most studies on emerging pattern mining focus on mining interesting categories of emerging patterns for classification and seldom discuss their application in trend prediction. To enable mine the set of emerging patterns with inherent item relations for decision making such as trend prediction, we employ a correlation test on the items of emerging patterns and introduce the productive emerging patterns as the set of emerging patterns with inherent item relations. We subsequently propose and develop PEPs, an efficient framework for mining our proposed productive emerging patterns. We further discuss and show the possible application of emerging patterns in trend prediction. Our experimental results shows PEPs is efficient, and the productive emerging pattern set which is smaller than the set of all emerging patterns, shows potentials in trend prediction.

Cite as: Nofong, V.M. (2015). Mining Productive Emerging Patterns and Their Application in Trend Prediction. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 109-117

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