Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases
In this work, we are presenting implementation details and extended scalability tests of the heuristic algorithm, which we had used in the past [1,2] to discover knowledge from multi-valued data entries stored in similarity-based fuzzy relational databases. The multi-valued symbolic descriptors, characterizing individual attributes of database records, are commonly used in similarity-based fuzzy databases to reflect uncertainty about the recorded observation. In this paper, we present an algorithm, which we developed to precisely interpret such non-atomic values and to transfer the fuzzy database tuples to the forms acceptable for many regular (i.e. atomic values based) data mining algorithms. (C) 2010 Elsevier Inc. All rights reserved.
similarity-based fuzzy relational databases data mining multi-valued entries taxonomic symbolic attributes fuzzy similarity relation