title: Unsupervised clustering algorithm for N-dimensional data.
abstract: Cluster analysis is an important tool for classifying data. Established techniques include k-means and k-median cluster analysis. However, these methods require the user to provide a priori estimations of the number of clusters and their approximate location in the parameter space. Often these estimations can be made based on some prior understanding about the nature of the data. Alternatively, the user makes these estimations based on visualization of the data. However, the latter is problematic in data sets with large numbers of dimensions. Presented here is an algorithm that can automatically provide these estimates without human intervention based on the inherent structure of the data set. The number of dimensions does not limit it.
authors.affiliation: Department of Neurology, National Primate Research Center, University of Wisconsin-Madison, 600 Highland Ave., Madison, WI 53792, USA. montgomery@neurology.wisc.edu
authors.forename: Erwin B
authors.initials: EB
authors.lastname: Montgomery
authors.forename: He
authors.initials: H
authors.lastname: Huang
authors.forename: Amir
authors.initials: A
authors.lastname: Assadi
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D012680:Sensitivity and Specificity