抄録
Identifying granular patterns of differentiation and learning predictors of product performance are key drivers to capitalize on competitive market segments. In this paper, we propose an approach to identify granular product patterns by using Hierarchical Clustering, and to learn predictors of product performance from historical data by using Genetic Programming. Computational experiments using more than twenty thousand vehicle models collected over the last thirty years shows (1) the feasibility to identify vehicle differentiation at different levels of granularity by hierarchical clustering, and (2) the good predictive ability of learned fuel consumption predictors in vehicle cluster. We believe our approach introduces the building blocks to further advance on studies regarding product differentiation and market segmentation by using data-intensive approaches.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 |
| 編集者 | Claudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato |
| 出版者 | IEEE Computer Society |
| ページ | 116-121 |
| ページ数 | 6 |
| 巻 | 2 |
| ISBN（電子版） | 9781538626665 |
| DOI | |
| 出版物ステータス | Published - 2018 6 8 |
| イベント | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan 継続期間: 2018 7 23 → 2018 7 27 |
Other
| Other | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 |
|---|---|
| 国 | Japan |
| 市 | Tokyo |
| 期間 | 18/7/23 → 18/7/27 |
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ASJC Scopus subject areas
- Software
- Computer Science Applications
これを引用
On Learning Fuel Consumption Prediction in Vehicle Clusters. / Parque Tenorio, Victor; Miyashita, Tomoyuki.
Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. 版 / Claudio Demartini; Sorel Reisman; Ling Liu; Edmundo Tovar; Hiroki Takakura; Ji-Jiang Yang; Chung-Horng Lung; Sheikh Iqbal Ahamed; Kamrul Hasan; Thomas Conte; Motonori Nakamura; Zhiyong Zhang; Toyokazu Akiyama; William Claycomb; Stelvio Cimato. 巻 2 IEEE Computer Society, 2018. p. 116-121 8377841.研究成果: Conference contribution
}
TY - GEN
T1 - On Learning Fuel Consumption Prediction in Vehicle Clusters
AU - Parque Tenorio, Victor
AU - Miyashita, Tomoyuki
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Identifying granular patterns of differentiation and learning predictors of product performance are key drivers to capitalize on competitive market segments. In this paper, we propose an approach to identify granular product patterns by using Hierarchical Clustering, and to learn predictors of product performance from historical data by using Genetic Programming. Computational experiments using more than twenty thousand vehicle models collected over the last thirty years shows (1) the feasibility to identify vehicle differentiation at different levels of granularity by hierarchical clustering, and (2) the good predictive ability of learned fuel consumption predictors in vehicle cluster. We believe our approach introduces the building blocks to further advance on studies regarding product differentiation and market segmentation by using data-intensive approaches.
AB - Identifying granular patterns of differentiation and learning predictors of product performance are key drivers to capitalize on competitive market segments. In this paper, we propose an approach to identify granular product patterns by using Hierarchical Clustering, and to learn predictors of product performance from historical data by using Genetic Programming. Computational experiments using more than twenty thousand vehicle models collected over the last thirty years shows (1) the feasibility to identify vehicle differentiation at different levels of granularity by hierarchical clustering, and (2) the good predictive ability of learned fuel consumption predictors in vehicle cluster. We believe our approach introduces the building blocks to further advance on studies regarding product differentiation and market segmentation by using data-intensive approaches.
KW - Clustering
KW - Fuel consumption estimation
KW - Genetic programming
KW - Prediction
KW - Vehicle
KW - Vehicle clustering
UR - http://www.scopus.com/inward/record.url?scp=85055502756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055502756&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2018.10214
DO - 10.1109/COMPSAC.2018.10214
M3 - Conference contribution
AN - SCOPUS:85055502756
VL - 2
SP - 116
EP - 121
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Demartini, Claudio
A2 - Reisman, Sorel
A2 - Liu, Ling
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Lung, Chung-Horng
A2 - Ahamed, Sheikh Iqbal
A2 - Hasan, Kamrul
A2 - Conte, Thomas
A2 - Nakamura, Motonori
A2 - Zhang, Zhiyong
A2 - Akiyama, Toyokazu
A2 - Claycomb, William
A2 - Cimato, Stelvio
PB - IEEE Computer Society
ER -