Detecting Frequent Patterns in Time Series Data using Partly Locality Sensitive Hashing
-
- Ogawara Koichi
- Kyushu University
-
- Tanabe Yasufumi
- Kyushu University
-
- Kurazume Ryo
- Kyushu University
-
- Hasegawa Tsutomu
- Kyushu University
Bibliographic Information
- Other Title
-
- 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
- Partly Locality Sensitive Hashing オ モチイタ ジケイレツ データ カラ ノ コウヒンド パターン チュウシュツ
Search this article
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 O (N 1+1/α) thanks to PLSH where N 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.
Journal
-
- Journal of the Robotics Society of Japan
-
Journal of the Robotics Society of Japan 29 (1), 67-76, 2011
The Robotics Society of Japan
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390282679701780096
-
- NII Article ID
- 10027648570
-
- NII Book ID
- AN00141189
-
- ISSN
- 18847145
- 02891824
-
- NDL BIB ID
- 10956820
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed