We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Detection of Linkage Patterns Repeating across Multiple Sequential Data

by Takahiro Miura, Yoshifumi Okada
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 3
Year of Publication: 2013
Authors: Takahiro Miura, Yoshifumi Okada
10.5120/10445-5136

Takahiro Miura, Yoshifumi Okada . Detection of Linkage Patterns Repeating across Multiple Sequential Data. International Journal of Computer Applications. 63, 3 ( February 2013), 14-17. DOI=10.5120/10445-5136

@article{ 10.5120/10445-5136,
author = { Takahiro Miura, Yoshifumi Okada },
title = { Detection of Linkage Patterns Repeating across Multiple Sequential Data },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 3 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number3/10445-5136/ },
doi = { 10.5120/10445-5136 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:13:10.966343+05:30
%A Takahiro Miura
%A Yoshifumi Okada
%T Detection of Linkage Patterns Repeating across Multiple Sequential Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 3
%P 14-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sequential data mining is a technology for acquiring useful information and patterns from large quantities of sequential data. Research into industrial and commercial applications of sequential data mining is flourishing. The aim of this study is to propose a new method for detecting groups of patterns that appear in a linked manner across multiple sequential data and repeat along a time axis. Such a set of pattern groups is called a "linkage pattern. " Linkage pattern is detected by using interval graphs that are generated from frequent patterns in multiple sequential data. The difference between this method and previous methods is that it does not assume similarity or correlation between patterns in different sequential data. If a pattern that frequently occurs in individual sequential data does not show similarity with patterns in different sequential data, these patterns will be detected as a linkage pattern as long as they are linked along a time axis. In this paper, this method is applied to artificial data with embedded linkage patterns and the detection accuracy is evaluated using three indexes (precision, recall, and F-measure). As a result, it is shown that embedded linkage patterns can be suitably detected and that detection accuracy increases as the window width for frequent pattern detection decreases.

References
  1. Tak-chungm F. 2011. A review on time series data mining. Engineering Applications of Artificial Intelligence. Volume 24, Issue 1. 164-181
  2. Zhao, Q. and Bhowmick, S. S. 2003. Sequential Pattern Mining: A Survey. Technical Report. CAIS. Nanyang Technological University. Singapore. No. 2003118.
  3. Mannila, H. , Toivonen, H. and Verkamo, A. I. 1997. Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1. 259-289.
  4. Ohtani, H. Kida, T. Uno, T and Arimura, H. Efficient Serial Episode Mining with Minimal Occurrences. 2009. The Third International Conference on Ubiquitous Information Management and Communication.
  5. Wen-Chi, P and Zhung-Xun, Liao. Mining sequential patterns across multiple sequence databases. 2009. Data & Knowledge Engineering Volume 68, Issue10. 1014-1033.
  6. Gong, C. Xindong, W. and Xingquan, Z. Mining Sequential Patterns across Time Sequences. 2008. New Generation Computing, 26. 75-96.
  7. Sakurai, Y. Faloutsos, C and Yamamuro, M. Stream monitoring under the time warping distance. 2007. In Proc. of ICDE. 1046-1055.
  8. Sakurai, Y. , Papadimitriou, S. and Faloutsos, C. 2005. BRAID: Stream Mining through Group Lag Correlations. In Proc. of ACM SIGMOD Conference. 599-610
  9. Zhu, Y. and Shasha, D. 2002. StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proc. Of VLDB. 358-369.
Index Terms

Computer Science
Information Sciences

Keywords

Sequential pattern mining Linkage pattern Interval graph