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Title:
Wardâs Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Wardâs Criterion? | Journal of Classification
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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. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Wardâs criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Wardâs hierarchical clustering method.
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- Wardâs Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Wardâs Criterion?
- Wardâs Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Wardâs Criterion?
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headline:Wardâs Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Wardâs Criterion?
description: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. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Wardâs criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Wardâs hierarchical clustering method.
datePublished:2014-10-18T00:00:00Z
dateModified:2014-10-18T00:00:00Z
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Hierarchical clustering
Ward
Lance-Williams
Minimum variance
Statistical software
Statistical Theory and Methods
Pattern Recognition
Bioinformatics
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Image and Speech Processing
Psychometrics
Marketing
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description: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. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Wardâs criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Wardâs hierarchical clustering method.
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Hierarchical clustering
Ward
Lance-Williams
Minimum variance
Statistical software
Statistical Theory and Methods
Pattern Recognition
Bioinformatics
Signal
Image and Speech Processing
Psychometrics
Marketing
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