GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data
Marja Ruotsalainen, Timo Ala-Kleemola, Ari J. E. Visa · 2007
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.