Duplicate record is a known problem within the datasets especially within databases of huge volumes. The accuracy of duplicates detection determines the efficiency of the duplicates removal process. Unfortunately, the effort to detect duplicates becomes more challenging due to the presence of missing values within the records. This is because, during the clustering and matching process, missing values can cause records that are similar to be assigned in a wrong group, causing the duplicates left undetected. In this paper, we present how duplicates detection can be improved even though missing values are present within a data set using our Duplicates Detection within the Incomplete Data set (DDID) method. We hypothetically add the missing values to the key attributes of two datasets under study, using an arbitrary pattern to simulate both complete and incomplete data sets. We analyze the results to evaluate the performance of duplicates detection using the Hot Deck method to compensate for the missing values in the key attributes. We hypothesize that by using Hot Deck, there is a performance improvement in duplicates detection. The performance of the DDID is compared with an early duplicates detection method (called DuDe) in terms of its accuracy and speed. The findings of the experiment show that, even though the data sets are incomplete, DDID is capable to offer better accuracy and faster duplicates detection as compared to a benchmark method (called DuDe). The results of this study contribute to duplicates detection under incomplete data sets constraint.