Missing Data in Collaborative Data Mining

Incomplete data in the data mining process is a common case. In collaborative data mining, treating this case can bring better performance and results, so the study of missing data from the perspective of the collaborative data mining approach is addressed in this article. Completing missing data for research sources have been done in two ways: using the average from other sources or adding an operator from the RapidMiner application to the processes. Both approaches have generated good results and can be considered as viable alternatives in collaborative data mining processes.