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Razavi Zadegan, S.M., Mirzaie, M. and Sadoughi, F. (2013) Ranked k-Medoids: A Fast and Accurate Rank-Based Partitioning Algorithm for Clustering Large Datasets. Knowledge-Based Systems, 39, 133-143.
https://doi.org/10.1016/j.knosys.2012.10.012

has been cited by the following article:

• TITLE: High Dimensional Cluster Analysis Using Path Lengths

  AUTHORS: Kevin Mcilhany, Stephen Wiggins

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

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.6 No.3, July 10, 2018

  ABSTRACT: 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.