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Encyclopedia of Database Systems pp 4556–4566Cite as

Visualizing Clustering Results

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Synonyms

Dendrogram; Heat map

Definition

Visualizing clusters is a way to facilitate human experts in evaluating, exploring, or interpreting the results of a cluster analysis. Clustering is an unsupervised learning technique, which groups a set of n data objects D = {x1, …, xn} into clusters so that objects in the same cluster are similar and objects from different clusters are dissimilar to each other. The data can be available (i) as (n × n) matrix of similarities (or dissimilarities), and (ii) as (n × d) data matrix, which describes each data object by a d-dimensional vector. The second form has to be accompanied by a suitable similarity or dissimilarity measure, which computes for a pair of d-dimensional vectors a (dis)similarity score. A typical example of such measure is the Euclidian metric. Clustering results may come in different forms: (i) as partition of D, (ii) as model, which summarizes properties of D, and (iii) as set of hierarchically nested partitions of D....

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Halle/Saale, Germany

    Alexander Hinneburg

Authors
  1. Alexander Hinneburg
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Corresponding author

Correspondence to Alexander Hinneburg .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

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Hinneburg, A. (2018). Visualizing Clustering Results. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_617

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