This paper discusses normalization of relations when the candidate keys of a relation have missing information represented by nulls. The paper shows that when the missing information is of the type " not applicable " or " does not exist, " problems and confusion can arise in normalizing relations. Candidate keys with missing information commonly are found in relations that represent information...

Real-life data sets are often incomplete, i.e., some attribute values are missing. In this paper we compare traditional, frequently used methods of handling missing attribute values, which are based on preprocessing, with another class of methods dealing with missing attribute values in which rule induction is performed directly on incomplete data sets, i.e., handling missing attribute values a...

In the computer-aided medical diagnosis, the problem of missing attribute values in many medical data sets brings a great challenge to data mining. To solve the problem, this paper proposes a method based on density clustering and grey relational analysis. It provides an effective solution for missing medical data. The method uses the characteristic and degree of data samples dynamic relation a...

While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulato...