Transcription of Clustering with Missing Values: No Imputation …
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Clustering with Missing Values: No Imputation …
Clustering with Missing Values: No ImputationRequiredKiri WagstaffJet Propulsion Laboratory, California Institute of Technology, 4800 Oak GroveDr., Pasadena, CA algorithms can identify groups in large data sets, such as starcatalogs and hyperspectral images. In general, Clustering methods cannot analyzeitems that have Missing data values. Common solutions either fill in the missingvalues ( Imputation ) or ignore the Missing data (marginalization). Imputed valuesare treated as just as reliable as the truly observed data, but they are only as good asthe assumptions used to create them. In contrast, we present a method for encodingpartially observed features as a set of supplemental soft constraints and introducethe KSC algorithm, which incorporates constraints into the Clustering process. Inexperiments on artificial data and data from the Sloan Digital Sky Survey, we showthat soft constraints are an effective way to enable Clustering with Missing IntroductionClustering is a powerful analysis tool that divides a set of items into a numberof distinct groups based on a problem-independent criterion, such as maximumlikelihood (the EM algorithm) or minimum variance (the k-means algorithm).
Clustering with Missing Values: No Imputation Required 3 to satisfy a set of hard constraints (Wagstaff et al., 2001). Hard constraints dictate that certain pairs of items must or must not be grouped together.
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