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

TL;DR

This paper evaluates the scalability of a heuristic algorithm designed to interpret non-atomic categorical attributes in fuzzy databases, emphasizing its linear scalability and ability to incorporate background knowledge for better data interpretation.

Contribution

The paper presents implementation details and scalability testing of a novel heuristic algorithm that interprets non-atomic fuzzy attributes and integrates background knowledge into the defuzzification process.

Findings

Algorithm exhibits linear scalability.

Incorporates background knowledge via fuzzy similarity hierarchy.

Effective in translating fuzzy tuples to atomic data for data mining.

Abstract

In this work we are analyzing scalability of the heuristic algorithm we used in the past 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.