PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series Tim Oates   View Paper (PDF)   Return to Main-Track Regular Papers Abstract This paper describes PERUSE, an unsupervised algorithm for finding recurring patterns in time series. It was initially developed and tested with sensor data from a mobile robot, i.e. noisy, re-valued, multivariate time series with variable intervals between observations. The pattern discovery problem is decomposed into two sub-problems: (1) a supervised learning problem in which a teacher provised exemplars of patterns and labels time series according to whether they contain the patterns; (2)an un supervised learning problem in which the time series are used to generate an approximation to the teacher. Experimental results show that PERUSE can discover patterns in audio data corresponding to qualitatively distinct outcomes of taking actions. DiSC'03 © 2003 Association for Computing Machinery