| Project/Area Number |
13680504
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
社会システム工学
|
|---|
| Research Institution | University of Tsukuba |
|---|
Principal Investigator |
KODA Masato Univ. Tsukuba, Inst. Policy Plan. Sciences, Professor, 社会工学系, 教授 (20114473)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SUZUKI Hideo Univ. Tsukuba, Inst. Policy Plan. Sci., Assoc. Prof., 社会工学系, 助教授 (10282328)
YOSHIDA Taketoshi JAIST, School of Knowledge Science, Assoc. Prof., 知能科学研究科, 助教授 (80293398)
|
|---|
| Project Period (FY) |
2001 – 2002
|
| Project Status |
Completed (Fiscal Year 2002)
|
| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2002: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2001: ¥2,400,000 (Direct Cost: ¥2,400,000)
|
|---|
| Keywords | Data Mining / Stochastic Sensitivity / Local Search / Scoring Model / TSP / ダイレクトマーケティング / 局所最適化 / CRM / 関数最適化 / 勾配法 / 局所探索法 |
|---|
| Research Abstract |
We have studied the synthesis and optimization of mathematical models for data mining to achieve higher performance, and obtained the following results :
1.A new stochastic optimization algorithm for discrete optimization :
Based on stochastic sensitivity analysis techniques, a novel optimization algorithm using Gaussian white noise is developed for a class of discrete optimization problems. Unlike the familiar local search algorithms, the proposed method does not require time-consuming metaheuristics. The algorithm generates sample points with Gaussian white noise and, and yields gradient information that are needed in local search. By intensive numerical studies, it is shown that the method is effective in finding solutions for noisy objective landscapes with inexact gradient information including traveling salesman problem (TSP).
2. Synthesis of data mining models to achieve high performance :
A technical framework is developed to assess the performance of data mining models and synthesize them in order to construct a mixture of experts. Based on the boosting techniques, the proposed framework enables synthesis of the near-optimal mixture. The numerical study and analysis are conducted on real business data and it is validated that the framework is effective in practice.
3.Applications to production scheduling and knowledge management :
Applications of data mining are studied to develop production scheduling and knowledge management. The aspect of knowledge discovery is especially explored to build soft systems methodology in order to incorporate knowledge-creation theory.
|
