Extraction of nonlinear structural change of time series from incomplete large-scale data
| Project/Area Number |
16500170
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Gunma University |
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Principal Investigator |
SEKI Yoichi Gunma University, Department of Engineering, Professor, 工学部, 教授 (90196949)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Data mining / Self-Organizing Maps / Clustering / Regression model / Multinomial logit model / MDL criterion / Marketing / Care service evaluation / セグメンテーション / 顧客セグメンテーション / 時系列データ / ロジスティック回帰モデル |
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| Research Abstract |
As theoretical viewpoints, we proposed the following models, and implemented the programs of these models with the statistical language S. In order to verify proposed methodology, we made case studies about care service evaluation data, POS (Point Of Sales) data with customer ID and Web access log data. In addition, we participated in the data analysis competition sponsored by the Operations Research Society of Japan etc..
1, We consider methods to extract types of individuals which have multivariate history. We use SOM (Self-Organizing Maps) to extract basic types of individuals at a specified point in time. Next, we define new distances between distributions of basic types on the SOM map using distribution functions. We then map the distributions of behavior types in order to obtain customer types over the long-term by SOM (Seki, et al. 2006). In addition, we consider a method to make index using spatial statistics.
2, We propose models which extract nonlinear structural changes in heterogeneous time series.
(1) We propose a model merge method, in order to obtain a segmentation whose segment has a homogeneous functional relation between variates.
(2) We generalize the SOM when there are two variable groups. We propose two stage SOM, which retains each structure of two variable groups, and extracts types of individuals.
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Report
(4 results)
Research Products
(11 results)
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[Journal Article] Prediction of care class by local additive reference to prototypical examples2005
Author(s)
Miyano, T., Tsutsui, T., Seki, Y., Higashino, S., Taniguchi, H.
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Journal Title
IEEE Transactions on Information Technology in Biomedicine 9,4
Pages: 502-507
Description
「研究成果報告書概要(和文)」より
Related Report
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[Journal Article] Prediction of care class by local additive reference to prototypical examples2005
Author(s)
Miyano, T., Tsutsuji, T., Seki, Y., Higashino, S., Taniguchi, H.
-
Journal Title
IEEE Transactions on Information Technology in Biomedicine 9, 4
Pages: 502-507
Description
「研究成果報告書概要(欧文)」より
Related Report
-
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