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CIDM
2007
IEEE

GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data

13 years 10 months ago
GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data
— This paper introduces a novel method, GAIS, for detecting interleaved sequential patterns from databases. A case, where data is of low quality and has errors is considered. Pattern detection from erroneous data, which contains multiple interleaved patterns is an important problem in a field of sensor network applications. We approach the problem by grouping data rows with the help of a model database and comparing groups with the models. In evaluation GAIS clearly outperforms the greedy algorithm. Using GAIS desired sequential patterns can be detected from low quality data.
Marja Ruotsalainen, Timo Ala-Kleemola, Ari Visa
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CIDM
Authors Marja Ruotsalainen, Timo Ala-Kleemola, Ari Visa
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