2013 Fiscal Year Final Research Report
Data Mining Methods Based on Closed Sets for Data Which Are Combination of Discrete and Continuous Attributes
| Project/Area Number |
22240010
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
HIRATA Kouich 九州工業大学, 情報工学研究院, 教授 (20274558)
TOKUNAGA Hiroo 首都大学東京, 理工学研究科, 教授 (30211395)
TSUIKI Hideki 京都大学, 人間・環境学研究科, 教授 (10211377)
HIROWATARI Eiju 北九州市立大学, 基盤教育センター, 教授 (60274429)
YOSHINAKA Ryo 京都大学, 情報学研究科, 助教 (80466424)
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| Co-Investigator(Renkei-kenkyūsha) |
SUGIYAMA Mahito 大阪大学, 産業科学研究所, 助教 (10733876)
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| Research Collaborator |
IKEDA Madori 京都大学, 情報学研究科, 博士課程
OTAKI Keisuke 京都大学, 情報学研究科, 博士課程
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Keywords | 知識発見 / 機械学習 / 形式概念解析 / 閉集合 / 実数計算 / 離散構造データ / フラクタル |
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| Research Abstract |
This research project is aiming at foundations of knowledge discovery algorithms for both discrete data and continuous data. The key concept of the foundations is closed set. We developed a semi-supervised algorithm for data which have both discrete attributes and continuous attributes. We applied the closed set method to combination of discrete data and continuous data from economics. We also developed new distances between discrete data, a new pseud-distance for continuous data, and a method diagnosis of outliers. We also applied binary coding to image retrieval. As theoretical results, we developed a computational learning theory based on Gray code, a computational learning theory of algebraic closed sets, a fast method of updating the lattice formed by fast closed sets.
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