Author(s)

Kevin Mcilhany¹, Stephen Wiggins²

¹Physics Department, US Naval Academy, Annapolis, MD, USA.
²School of Mathematics, University of Bristol, Bristol, UK.

A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions (). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.

Clustering, Path Length, Consensus, N-Dimensional, Line of Sight

Mcilhany, K. and Wiggins, S. (2018) High Dimensional Cluster Analysis Using Path Lengths. Journal of Data Analysis and Information Processing, 6, 93-125. doi: 10.4236/jdaip.2018.63007.