@inProceedings{
 title = {Routine learning: analyzing your whereabouts.},
 type = {inProceedings},
 year = {2004},
 pages = {208-212},
 publisher = {Proc. International Conference on Information Technology (ITCC 2004), Las Vegas, NV, USA},
 id = {afcc96da-6e07-3f6a-88ff-5f02a265e8ef},
 created = {2019-11-19T13:00:38.631Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
 last_modified = {2019-11-19T13:46:13.943Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {isg:495},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {A routine is a temporal context sequence that occurs often. In routine learning, already recognized contexts are utilized in modeling the user behavior. The methodology is presented via a use case scenario. Data collected from various ubiquitous sensors are used in recognizing and defining contexts, and association rules determine the routines. The focus is on testing the suitability of the Apriori algorithm for this application area. Several useful routines were derived from the user data, and the results show that data mining can be utilized in pervasive computing.},
 bibtype = {inProceedings},
 author = {Pirttikangas S, Riekki J & Röning J}
}

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