Abstract: Described are techniques for detecting labels incorrectly assigned to data set fields. The data of each data set field, such as those data set fields assigned to the same label, are represented using a set of characteristics. The data set fields are then clustered into clusters based on the characteristics of the data of the data set fields. Those clusters of data set fields with a homogeneity (being assigned the same label) that exceeds a first threshold value and is below a second threshold value are identified. One or labels assigned to the data set fields of the identified clusters are identified as being suspect for incorrect assignments by having a frequency below a third threshold value (e.g., 3%), which may be user-designated. The label(s) identified as being suspect for incorrect assignment are then presented to a user for review.