Partitioning cluster analyses are powerful tools for rapidly and objectively exploring and characterizing disparate geophysical databases with unknown interrelations between individual data sets or models. Despite its high potential to objectively extract the dominant structural information from suites of disparate geophysical data sets or models, cluster-analysis techniques are underused when analyzing geophysical data or models. This is due to the following limitations regarding the applicability of standard partitioning cluster algorithms to geophysical databases: The considered survey or model area must be fully covered by all data sets; cluster algorithms classify data in a multidimensional parameter space while ignoring spatial information present in the databases and are therefore sensitive to high-frequency spatial noise (outliers); and standard cluster algorithms such asfuzzy c-means (FCM) or crisp k-means classify data in an unsupervised manner, potentially ignoring expert knowledge additionally available to the experienced human interpreter. We address all of these issues by considering recent modifications to the standard FCM cluster algorithm to tolerate incomplete databases, i.e., survey or model areas not covered by all available data sets, and to consider spatial information present in the database. We have evaluated the regularized missing-value FCM cluster algorithm in a synthetic study and applied it to a database comprising partially colocated crosshole tomographic P- and S-wave-velocity models. Additionally, we were able to demonstrate how further expert knowledge can be incorporated in the cluster analysis to obtain a multiparameter geophysical model to objectively outline the dominant subsurface units, explaining all available geoscientific information.