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|Title:||Combining two data mining methods for system identification||Authors:||Saitta, S.
|Issue Date:||2006||Citation:||Saitta, S.,Raphael, B.,Smith, I.F.C. (2006). Combining two data mining methods for system identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4200 LNAI : 606-614. ScholarBank@NUS Repository.||Abstract:||System identification is an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, instead of optimizing values of parameters within one behavior model, system identification is supported by multi-model reasoning strategies. The objective of this work is to develop a data mining algorithm that combines principal component analysis and k-means to obtain better understandings of spaces of candidate models. One goal is to improve views of model-space topologies. The presence of clusters of models having the same characteristics, thereby defining model classes, is an example of useful topological information. Distance metrics add knowledge related to cluster dissimilarity. Engineers are thus better able to improve decision making for system identification and downstream tasks such as further measurement, preventative maintenance and structural replacement. © Springer-Verlag Berlin Heidelberg 2006.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/45921||ISBN:||3540462465||ISSN:||03029743|
|Appears in Collections:||Staff Publications|
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