Partly Locality Sensitive Hashing を用いた時系列データからの高頻度パターン抽出

Translated title of the contribution: Detecting Frequent Patterns in Time Series Data using Partly Locality Sensitive Hashing

小川原 光一, 田邉 康史, 倉爪 亮, 長谷川 勉

Research output: Contribution to journalArticlepeer-review

Abstract

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 contributionDetecting Frequent Patterns in Time Series Data using Partly Locality Sensitive Hashing
Original languageJapanese
Pages (from-to)67-76
Number of pages10
Journal日本ロボット学会誌
Volume29
Issue number1
DOIs
Publication statusPublished - Jan 15 2011

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