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- W137256923 abstract "We propose an activity discovery framework that aims at identifying activities within data streams in the absence of data annotation. The process starts with dividing the full sensor stream into segments by identifying differences in sensor activations characterizing potential activity changes. Then, extracted segments are clustered in order to find groups of similar segments each representing a candidate activity. Lastly, parameters of a sequential labeling algorithm are estimated using segment clusters found in the previous step and the learned model is used to smooth the initial segmentation. We present experimental evaluation for two real world datasets. The results obtained show that our segmentation approaches perform almost as good as the true segmentation and that activities are discovered with a high accuracy in most of the cases. We demonstrate the effectiveness of our model by comparing it with a technique using substantial domain knowledge." @default.
- W137256923 created "2016-06-24" @default.
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- W137256923 date "2013-01-01" @default.
- W137256923 modified "2023-10-16" @default.
- W137256923 title "A Fully Unsupervised Approach to Activity Discovery" @default.
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- W137256923 doi "https://doi.org/10.1007/978-3-319-02714-2_7" @default.
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