Recognition of Emergent Human Behaviour in a Smart Home: A Data Mining Approach
MetadataShow full item record
Copyright 2007 Elsevier B.V. All rights reserved
Motivated by a growing need for intelligent housing to accommodate ageing populations, we propose a novel application of intertransaction association rule (IAR) mining to detect anomalous behaviour in smart home occupants. An efficient mining algorithm that avoids the candidate generation bottleneck limiting the application of current IAR mining algorithms on smart home data sets is detailed. An original visual interface for the exploration of new and changing behaviours distilled from discovered patterns using a new process for finding emergent rules is presented. Finally, we discuss our observations on the emergent behaviours detected in the homes of two real world subjects.
Showing items related by title, author, creator and subject.
A Knapsack Problem Approach For Achieving Efficient Energy Consumption in Smart Grid for Endusers’ Life StyleSianaki, Omid; Hussain, Omar; Tabesh, A. (2010)In order to achieve an efficient energy consumption level in the residential sector of a smart grid, the end-users are equipped with various smart home energy controller technologies. The devices are provided to inform ...
Luhr, Sebastian; Venkatesh, Svetha; West, Geoffrey (2005)This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments ...
Rathnayaka, Dinusha; Potdar, Vidyasagar; Kuruppu, S. (2011)Residential sector registers one of the highest energy consumers in the world; hence efficient Energy Resource Management (ERM) scheme has become essential in new home or renovation projects. Smart home concept is an ...