@inproceedings{0dd3b6f5c94841a1bf180d4581d48222,
title = "On Learning Fuel Consumption Prediction in Vehicle Clusters",
abstract = "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.",
keywords = "Clustering, Fuel consumption estimation, Genetic programming, Prediction, Vehicle, Vehicle clustering",
author = "Victor Parque and Tomoyuki Miyashita",
year = "2018",
month = jun,
day = "8",
doi = "10.1109/COMPSAC.2018.10214",
language = "English",
series = "Proceedings - International Computer Software and Applications Conference",
publisher = "IEEE Computer Society",
pages = "116--121",
editor = "Claudio Demartini and Sorel Reisman and Ling Liu and Edmundo Tovar and Hiroki Takakura and Ji-Jiang Yang and Chung-Horng Lung and Ahamed, {Sheikh Iqbal} and Kamrul Hasan and Thomas Conte and Motonori Nakamura and Zhiyong Zhang and Toyokazu Akiyama and William Claycomb and Stelvio Cimato",
booktitle = "Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018",
note = "42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 ; Conference date: 23-07-2018 Through 27-07-2018",
}