Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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The relationship between occupant activity and electricity consumption is inextricably linked. It has been difficult to both gather detailed energy data and information about occupants' daily lives as well as understand their relationship quantitatively. There is significant past work on activity recognition in homes and load prediction, but there is limited understanding of how activities can inform consumption or vice versa. Our work begins by characterizing power data as provided by plug-level meters from one household. Association and sequential rule mining techniques are applied to extract explicit rules that may be useful for forming the basis of demand patterns. Initial findings include the identification of device groups but highlight the challenges of modeling complex patterns and event rarity.