Fuzzy Concept Mining based on Formal Concept Analysis

Data Mining(also known as Knowledge Discovery) is defined as the non-trivial extraction of implicit, previously unknown, and potentially useful information from data. It includes not only methods for extracting information from the given data, but also visualizing the information. Formal Concept Analysis(FCA) is one of Data mining research fields, and it has been applied to a number of areas such as medicine, psychology, library, information science, and software re-engineering and others. FCA is based on a mathematical order theory for data analysis, which extracts concepts and builds a conceptual hierarchy from given data. In order to analyze vague data set of uncertainty information, Fuzzy Formal Concept Analysis(Fuzzy FCA) incorporates fuzzy set theory into FCA. In this paper, we introduce basic notions of FCA and Fuzzy FCA, and developed the Fuzzy FCA-Wizard, that supports Fuzzy FCA’s features. We demonstrate the process for discovering knowledge from uncertain data with Fuzzy FCA-Wizard. Keywords— Data Mining, Knowledge Discovery, Fuzzy Set, Formal Concept Analysis