| Project/Area Number |
25330256
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | University of Yamanashi |
|---|
Principal Investigator |
IWANUMA Koji 山梨大学, 総合研究部, 教授 (30176557)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
山本 泰生 山梨大学, 総合研究部, 助教 (30550793)
|
|---|
| Project Period (FY) |
2013-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | データマイニング / 負の相関ルール / 極小生成子 / 飽和アイテム集合 / オンライン型アルゴリズム / データストリーム / 潜在因子 / 無損失圧縮 / 近似計算 / アイテム集合系列 / 潜在因子発見 / 頻出アイテム集合 / 系列データ |
|---|
| Outline of Final Research Achievements |
In this research, we developed several efficient algorithms for negative association rule mining, which can be regraded as a concrete form of a latent association mining. We also studied some online approximation algorithms for a huge transaction stream, which is an essential tool for generating latent event sequence corpora from a very large sequential text data such as newspaper data. The details are as follows: First, we proposed a new efficient top-down search algorithm for valid negative association rules, which uses a suffix tree over frequent itemsets. Second, we studied lossless compression for a set of negative rules, and gave a novel lossless compression method based on minimal generators. Third, we developed two online approximation algorithms for mining a huge transaction stream: one achieves a resource-oriented computation, and another uses an incremental intersection computation for frequent closed itemsets, both of which can avoid the combinatorial explosion phenomena.
|
