Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Designing Smart Homes
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Data mining techniques have been vastly exploited recently to overcome complex problems that humans struggle to solve. Particularly, the recognition of the activity of daily living of a smart home's resident is a challenging issue that requires advanced algorithms using extensive plans' library. In this paper, we propose a novel unsupervised learning technique for the discovery of sequential pattern related to spatial relationships of objects inside a smart home. We concretely use this approach to automatically construct a library of plans. Finally, we demonstrate the efficiency with a practical activity recognition algorithm by comparing learned knowledge over expert's defined library in a real smart home.