@inproceedings{e564f3c3d01a4f2b9c5138e96acfd4da,
title = "Capturing emergent behavior in multi-response systems through data trend mining",
abstract = "This paper presents a novel approach to capture emerging systems behavior involving multiple performance criteria. Due to the interactions that exist among systems, engineers may be faced with a multi-objective design space that current single response data mining models do not capture. We aim to address this challenge by proposing a Multi-Response Trend Mining algorithm that simultaneously predicts multiple performance objectives by identifying the time series behavior of the individual systems. The proposed approach is a departure from conventional data mining approaches that are often limited to evaluating single response variables in a given static data set. The resulting system level predictions will serve as performance targets for next generation systems design efforts. The Multi- Response Trend Mining model can then be integrated with multi-objective engineering models during the systems design and analysis phase so that engineering design solutions better reflect emerging system performance trends. A vehicle design data set from the UC Irvine Machine Learning Repository is used to validate the proposed methodology and highlight the need for multi-response predictive algorithms in systems design.",
author = "Tucker, {Conrad S.} and Kim, {Harrison M.}",
year = "2010",
month = dec,
day = "1",
doi = "10.2514/6.2010-9322",
language = "English (US)",
isbn = "9781600869549",
series = "13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010",
booktitle = "13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010",
note = "13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 ; Conference date: 13-09-2010 Through 15-09-2010",
}