In real world data analysis, missing values may be caused by many different reasons. The data values may be unknown (because not measured) or non-sense (such as husband's age if asked non-married respondent). We argue that both cases carry a different type of information that may not be confused. A careful handling is needed instead. The objective of this presentation is to discuss work in progress on the topic of processing various types of unavailable information within association analysis.