Frequent patterns in time series data are useful clues to learn previously unknown events in an unsupervised way. In this paper, we propose a method for detecting frequent patterns in long time series data efficiently. The major contribution of the paper is two-fold: (1) Partly Locality Sensitive Hashing (PLSH) is proposed to find frequent patterns efficiently and (2) the problem of finding consecutive time frames that have a large number of frequent patterns is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time <i>O</i> (<i>N</i> <sup>1+1/α</sup>) thanks to PLSH where <i>N</i> is the total amount of data. The proposed method was evaluated by detecting frequent whole body motions in a video sequence as well as by detecting frequent everyday manipulation tasks in motion capture data.
|Translated title of the contribution||Detecting Frequent Patterns in Time Series Data using Partly Locality Sensitive Hashing|
|Number of pages||10|
|Publication status||Published - Jan 15 2011|