I have just been informed that we received the 2008 best paper award for our article in the Journal of Computing in Civil Engineering (JCCE). The paper we wrote is entitled “Improving System Identification using Clustering”. I have written the paper with Dr. Prakash Kripakaran, Dr. Benny Raphael and Prof. Ian F.C. Smith. The paper is not a breakthrough in data mining, but an example of application in civil engineering. Here is the abstract:
System identification involves identification of a behavioral model that best explains the measured behavior of a structure. This research uses a strategy of generation and iterative filtering of multiple candidate models for system identification. The task of model filtering is supported by measurement-interpretation cycles. During each cycle, the location for subsequent measurement is chosen using the predictions of current candidate models. In this paper, data mining techniques are proposed to support such measurement-interpretation cycles. Candidate models, representing possible states of a structure, are clustered using a technique that combines principal component analysis and K-means clustering. Representative models of each cluster are used to place sensors for subsequent measurement on the basis of the entropy of their predictions. Results show that clustering is necessary to identify the different groups of candidate models. The entropy of predictions is found to be a valid stopping criterion for iterative sensor addition. Clustering helps classify models and, thus, it provides useful support to engineers for further decision making.
Update: Due to the number of requests for the paper, I have put it on my website. You can now directly download it: Improving System Identification using Clustering (pdf)