32
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm

      Preprint
      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. However there are different interpretations in the literature and there are different implementations of the Ward agglomerative algorithm in commonly used software systems, including differing expressions of the agglomerative criterion. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward's hierarchical clustering method.

          Related collections

          Most cited references3

          • Record: found
          • Abstract: not found
          • Article: not found

          A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              256. Note: An Algorithm for Hierarchical Classifications

                Bookmark

                Author and article information

                Journal
                2011-11-27
                2011-12-11
                Article
                10.1007/s00357-014-9161-z
                1111.6285
                442e6dae-9160-455b-a1e0-b46fccc6f132

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                62H30, 91C20
                20 pages, 21 citations, 4 figures
                stat.ML cs.CV stat.AP

                Computer vision & Pattern recognition,Applications,Machine learning
                Computer vision & Pattern recognition, Applications, Machine learning

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content302

                Cited by90

                Most referenced authors75