Hunter, Andrew J. S.Ortiz Villagomez, Andres R.2017-12-182017-12-182012http://hdl.handle.net/1880/105740Bibliography: p. 145-175A few pages are in colour.Recent advances in GPS collar technologies for Grizzly bear tracking have produced a drastic increase in the volume of data available for scientific analysis. Machine learning methods seem suited to process this ever-increasing volume of data. Comprehensive understanding of the datasets, machine learning methods and similarity measures is fundamental for research of this kind. To automatically detect frequent movement patterns, the current work implemented three machine learning methods, a Location-Based Services (LBS), a simulated annealing, and a hybrid local alignment approach. Several dataset segmentations were tested to reduce the amount of calculations for similarity measures, without losing relevant data relationships. Mostly based on my fifteen yea.rs of professional experience in the industry of data.base administration and development, I found the current state of commercial data.base man­agement systems (DBMS) mature enough to conduct fully integrated implementations. In my judgment, that assumption was validated by the results of the local alignment method.xxiii, 246 leaves : ill. ; 30 cm.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.master thesis10.11575/PRISM/4739