Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.pmcj.2006.08.002 | Pervasive and Mobile Computing |
Keywords | Field | DocType |
intertransaction association rules,efficient mining algorithm,emergent behaviour,visual data mining,smart homes,candidate generation bottleneck,smart home data set,emergent human behaviour,smart home occupant,emergent rule,data mining approach,current iar mining algorithm,new process,novel application,ageing population,anomalous behaviour,association rule,smart home,human behaviour,data mining | Data science,Data mining,Bottleneck,Visual interface,Computer science,Home automation,Association rule learning,Data mining algorithm,Limiting | Journal |
Volume | Issue | ISSN |
3 | 2 | Pervasive and Mobile Computing |
Citations | PageRank | References |
31 | 1.50 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Lühr | 1 | 123 | 8.04 |
| Geoff A. W. West | 2 | 562 | 82.46 |
| Svetha Venkatesh | 3 | 4190 | 425.27 |