Center for Avian Cognition
Date of this Version
5-2005
Citation
2005 IEEE International Conference on Electro Information Technology, Lincoln, NE, USA, 22-25 May 2005. DOI: 10.1109/EIT.2005.1626978
Abstract
Clustering is a practical data mining approach of pattern detection. Because of the sensitivity of initial conditions, k-means clustering often suffers from low clustering performance. We present a procedure to refine initial conditions of k-means clustering by analyzing density distributions of a data set before estimating the number of clusters k necessary for the data set, as well as the positions of the initial centroids of the clusters. We demonstrate that this approach indeed improves the accuracy and performance of k-means clustering measured by average intra to interclustering error ratio. This method is applied to the virtual ecology project to design a virtual blue jay system.
Included in
Animal Studies Commons, Behavior and Ethology Commons, Cognition and Perception Commons, Forest Sciences Commons, Ornithology Commons, Other Psychology Commons
Comments
Copyright (cO) 2015 Guanshan Yu, Leen-Kiat Soh, & Alan Bond