Duration in contest clustering for speech recognition
Speech Communication
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Principles of data mining
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
Data mining techniques for improving the reliability of system identification
Advanced Engineering Informatics
A hybrid method for robust car plate character recognition
Engineering Applications of Artificial Intelligence
An efficient greedy K-means algorithm for global gene trajectory clustering
Expert Systems with Applications: An International Journal
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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.