| Project/Area Number |
23500182
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
SHOUDAI Takayoshi 九州大学, システム情報科学研究科(研究院, 准教授 (50226304)
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| Co-Investigator(Kenkyū-buntansha) |
UCHIDA Tomoyuki 広島市立大学, 大学院・情報科学研究科, 准教授 (70264934)
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| Co-Investigator(Renkei-kenkyūsha) |
MIZOGUCHI Yoshihiro 九州大学, マスフォアインダストリ研究所, 准教授 (80209783)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2011: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | グラフパターン / グラフマイニング / グラフアルゴリズム / グラフ構造データ / データマイニング / 機械学習 / 機械発見 / 帰納推論 |
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| Research Abstract |
Our object of this research is to develop an effective graph pattern designing system for efficient data mining from graph-structured data. During this research period, the following results mainly were obtained.
(1) A tree contraction pattern (TC-pattern) is an unordered tree-structured pattern common to given unordered trees, which is obtained by merging every uncommon connected substructure into one vertex by edge contraction. We show that an important subclass of TC-patterns is polynomial-time inductively inferable from positive data. Moreover, we discuss the optimization versions of the learning problems for TC-patterns, and give the conditions under which the optimization problems are hard to compute.
(2) We introduce context-deterministic regular formal graph systems (FGS) as one of the effective graph pattern designing systems, and propose a polynomial time algorithm for learning the class of context-deterministic regular FGSs in the framework of MAT learning.
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