Principles of data mining
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Top-Down Motif Discovery in Biological Sequence Datasets by Genetic Algorithm
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
A Weighted Distance Measure for Calculating the Similarity of Sparsely Distributed Trajectories
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
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The mining of meaningful shapes of time series is done widely in order to find shapes that can be used, for example, in classification problems or in summarizing signals. Normally, shapes that summarize periodic signals have to be mined visually, and in order to find a shape of high quality, several tests haves to be made. This makes visual mining slow and sometimes even frustrating. A method for summarizing a periodic time series automatically is presented in this study. The method is based on evolutionary computation and the results show that by using it, shapes can be found that summarize a time series better than shapes found using visual mining.