Clustering with Missing Values : No Imputation Required

  • Wagstaff K
  • 70


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in


  • Kiri Wagstaff

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free