Presentation 2005-05-31
Narrowing Frequent Patterns by Relation Strength in Sequencial Pattern Mining
Naoki OHTSUKA, Koji IWANUMA, Hidetomo NABESHIMA,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) Takano at al proposed the total frequency measure that finds out all frequent sequential patterns in a single large-scale data sequence. But, this measure extracts too many frequent sequential patterns which may include noisy patterns. In this paper, we propose a method that can narrow frequent patterns with a relation strength like the mutual information between elements in a sequence. This method drastically reduces the number of frequent sequential patterns. and the ratio of remaining valid sequential patterns increased.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Data Mining / Sequential Pattern Mining / Mutual Information
Paper # AI2005-5
Date of Issue

Conference Information
Committee AI
Conference Date 2005/5/24(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Narrowing Frequent Patterns by Relation Strength in Sequencial Pattern Mining
Sub Title (in English)
Keyword(1) Data Mining
Keyword(2) Sequential Pattern Mining
Keyword(3) Mutual Information
1st Author's Name Naoki OHTSUKA
1st Author's Affiliation Computer Science and Media Engineering, Interdisciplinary, Graduate School of Medicine and Engineering, University of Yamanashi()
2nd Author's Name Koji IWANUMA
2nd Author's Affiliation Interdiscipinary Graduate School of Medicine and Engineering, University of Yamanashi
3rd Author's Name Hidetomo NABESHIMA
3rd Author's Affiliation Interdiscipinary Graduate School of Medicine and Engineering, University of Yamanashi
Date 2005-05-31
Paper # AI2005-5
Volume (vol) vol.105
Number (no) 105
Page pp.pp.-
#Pages 6
Date of Issue