Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
85314 | Computers and Electronics in Agriculture | 2007 | 14 Pages |
Abstract
This paper describes an application of the K-means classification algorithm to categorize animal tracking data into various classes of behavior. It was found that, even without explicit consideration of biological factors, the clustering algorithm repeatably resolved tracking data from cows into two groups corresponding to active and inactive periods. Furthermore, it is shown that this classification is robust to a large range of data sampling intervals. An adaptive data sampling algorithm is suggested for improving the efficiency of both energy and memory usage in animal tracking equipment.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Mac Schwager, Dean M. Anderson, Zack Butler, Daniela Rus,