Partial periodic patterns mining with multiple minimum supports

Kung Jiuan Yang; Guo Cheng Lan; Tzung Pei Hong; Yuh Min Chen

doi:10.1109/ICICS.2013.6782910

Partial periodic patterns mining with multiple minimum supports

Kung Jiuan Yang
, Guo Cheng Lan
, Tzung Pei Hong
, Yuh Min Chen

Institute of Manufacturing Information and Systems

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

Partial periodic patterns are commonly seen in real-world applications. Most of the previous approaches set a single minimum support threshold for all the events in a sequence. However using only one minimum support for all events in an event sequence to assume they have similar frequencies is not easy to happen in real-life applications. In this paper, we propose an algorithm which applies the projection-based mechanism and specifies multiple minimum supports to effectively discover appropriate partial periodic patterns. Finally, the experimental result shows the good performance of the proposed approach.

Original language	English
DOIs	https://doi.org/10.1109/ICICS.2013.6782910
Publication status	Published - 2013
Event	9th International Conference on Information, Communications and Signal Processing, ICICS 2013 - Tainan, Taiwan Duration: 2013 Dec 10 → 2013 Dec 13

Other

Other	9th International Conference on Information, Communications and Signal Processing, ICICS 2013
Country/Territory	Taiwan
City	Tainan
Period	13-12-10 → 13-12-13

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Access to Document

10.1109/ICICS.2013.6782910

Cite this

@conference{092fa266cafd4d9d919edb27798fa71f,

title = "Partial periodic patterns mining with multiple minimum supports",

abstract = "Partial periodic patterns are commonly seen in real-world applications. Most of the previous approaches set a single minimum support threshold for all the events in a sequence. However using only one minimum support for all events in an event sequence to assume they have similar frequencies is not easy to happen in real-life applications. In this paper, we propose an algorithm which applies the projection-based mechanism and specifies multiple minimum supports to effectively discover appropriate partial periodic patterns. Finally, the experimental result shows the good performance of the proposed approach.",

author = "Yang, \{Kung Jiuan\} and Lan, \{Guo Cheng\} and Hong, \{Tzung Pei\} and Chen, \{Yuh Min\}",

year = "2013",

doi = "10.1109/ICICS.2013.6782910",

language = "English",

note = "9th International Conference on Information, Communications and Signal Processing, ICICS 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

}

TY - CONF

T1 - Partial periodic patterns mining with multiple minimum supports

AU - Yang, Kung Jiuan

AU - Lan, Guo Cheng

AU - Hong, Tzung Pei

AU - Chen, Yuh Min

PY - 2013

Y1 - 2013

N2 - Partial periodic patterns are commonly seen in real-world applications. Most of the previous approaches set a single minimum support threshold for all the events in a sequence. However using only one minimum support for all events in an event sequence to assume they have similar frequencies is not easy to happen in real-life applications. In this paper, we propose an algorithm which applies the projection-based mechanism and specifies multiple minimum supports to effectively discover appropriate partial periodic patterns. Finally, the experimental result shows the good performance of the proposed approach.

AB - Partial periodic patterns are commonly seen in real-world applications. Most of the previous approaches set a single minimum support threshold for all the events in a sequence. However using only one minimum support for all events in an event sequence to assume they have similar frequencies is not easy to happen in real-life applications. In this paper, we propose an algorithm which applies the projection-based mechanism and specifies multiple minimum supports to effectively discover appropriate partial periodic patterns. Finally, the experimental result shows the good performance of the proposed approach.

UR - https://www.scopus.com/pages/publications/84899087019

UR - https://www.scopus.com/pages/publications/84899087019#tab=citedBy

U2 - 10.1109/ICICS.2013.6782910

DO - 10.1109/ICICS.2013.6782910

M3 - Paper

AN - SCOPUS:84899087019

T2 - 9th International Conference on Information, Communications and Signal Processing, ICICS 2013

Y2 - 10 December 2013 through 13 December 2013

ER -

Partial periodic patterns mining with multiple minimum supports

Abstract

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this