Knowledge Discovery from Large-scale Text Sequences by Integrating Sequential Data Mining and Advanced Reasoning
Project/Area Number |
22500127
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | University of Yamanashi |
Principal Investigator |
IWANUMA Koji 山梨大学, 大学院・医学工学総合研究部, 教授 (30176557)
|
Co-Investigator(Kenkyū-buntansha) |
YAMAMOTO Yoshitaka 山梨大学, 大学院・医学工学総合研究部, 助教 (30550793)
|
Project Period (FY) |
2010 – 2012
|
Project Status |
Completed (Fiscal Year 2012)
|
Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2011: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2010: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
Keywords | データマイニング / オンライン型アルゴリズム / 近似アルゴリズム / 相関ルール / 潜在的因子 / コーパス / テキスト系列 / 負の相関ルール / 潜在的因子発見 / 頻出アイテム集合 / 系列データ / 系列データマイニング / テキスト / 圧縮 / 仮説推論 / オンラインアルゴリズム / 情報量 / 学習 / 空間計算量 |
Research Abstract |
In this research, we developed some new fast and effective sequential data-mining technology for the knowledge discovery from series of large-scale text data. We also proposed a new method for structuring and compressing a huge amount of datawhich are extracted in data-mining process. Furthermore, we gave some new algorithms for extracting latent rules in the form of negative association rule mining, and also for discovering missing factors in the form of inductive reasoning. We study these issues not only from a theoretical viewpoint but also from an experimental evaluation.
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Report
(4 results)
Research Products
(37 results)