Using Pervasive Computing to Deliver Elder Care
IEEE Pervasive Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Proceedings of the 6th international conference on Information processing in sensor networks
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
On constructing optimistic simulation algorithms for the discrete event system specification
ACM Transactions on Modeling and Computer Simulation (TOMACS)
IEEE Transactions on Consumer Electronics
Engineering Applications of Artificial Intelligence
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
Context-aware inference in ubiquitous residential environments
Computers in Industry
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
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More convenient smart home environments can be constructed by monitoring home appliances, if automated services are supported by their usage information. This paper proposes a scheme for translating association rules among appliances mined from their usage information into service scenarios. A smart home environment is unique in that there exist a limited number of home appliances, some of which operate without interruption like a refrigerator. Furthermore, the number of home appliances is much less than the number of items in itemsets to which existing algorithms for mining association rules have been applied. After showing that the existing algorithms are limited in improving the usefulness of association rule generation by means of adjusting the confidence level due to such unique characteristics, we propose a new service scenario generation scheme which calculates the confidence level based on hypothesis testing. This paper demonstrates that association rule mining based on the dependence between appliances is feasible and its performance is very much comparable with that of an existing association rule mining algorithm. Since home users are allowed to choose their favorable scenarios from meaningful service scenarios generated by the proposed scheme without their intervention, we expect that future smart homes can provide automated services more efficiently to them, especially to surveillance systems for elderly.