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CORR
2011
Springer

Heuristic Algorithm for Interpretation of Non-Atomic Categorical Attributes in Similarity-based Fuzzy Databases - Scalability Ev

10 years 5 months ago
Heuristic Algorithm for Interpretation of Non-Atomic Categorical Attributes in Similarity-based Fuzzy Databases - Scalability Ev
- In this work we are analyzing scalability of the heuristic algorithm we used in the past [1-4] to discover knowledge from multi-valued symbolic attributes in fuzzy databases. The non-atomic descriptors, characterizing a single attribute of a database record, are commonly used in fuzzy databases to reflect uncertainty about the recorded observation. In this paper, we present implementation details and scalability tests of the algorithm, which we developed to precisely interpret such non-atomic values and to transfer (i.e. defuzzify) the fuzzy tuples to the forms acceptable for many regular (i.e. atomic values based) data mining algorithms. Important advantages of our approach are: (1) its linear scalability, and (2) its unique capability of incorporating background knowledge, implicitly stored in the fuzzy database models in the form of fuzzy similarity hierarchy, into the interpretation/defuzzification process.
M. Shahriar Hossain, Rafal A. Angryk
Added 19 Aug 2011
Updated 19 Aug 2011
Type Journal
Year 2011
Where CORR
Authors M. Shahriar Hossain, Rafal A. Angryk
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