A New Approach to Constructing Maximal Consistent Blocks for Mining Incomplete Data

Abstract

A maximal consistent block technique is used for mining incomplete data sets. Initially, the idea of maximal consistent blocks was introduced for data sets with missing attribute values interpreted as “do not care” conditions. In such data, missing attribute values may be replaced by any existing attribute value. In this paper, we analyze existing algorithms for computing maximal consistent blocks and show that some algorithms may output blocks that are not maximal, especially for data sets with non-transitive characteristic relations. We also introduce a new approach for computing maximal consistent blocks from incomplete data, with two interpretations of missing attribute values: lost values and “do not care” conditions. We indicated that the obtained consistent blocks are maximal and our approach is faster than the commonly used method of generating maximal consistent blocks based on characteristic relation.