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This paper is devoted to fuzzy association analysis. Our motivation to this work arose from mining of linguistic associations, however found relations among mined associations are valid in general. Three the most commonly used support and confidence measures are considered and various relations among found and previously known associations given by confirmation measures are studied. Good understanding of such relationships is essential for creating more efficient algorithms, for subsequent work with found associations as well as for cooperation with the consumer of the data mining process.

In this contribution we provide an application of the technique of F-transform. We demonstrate that by using a simple density-based preprocessing, the applicability of F-transform in data analysis can be significantly improved. Despite of the fact that our procedure is demonstrated by a well-known DB-SCAN algorithm and the technique of F-transform, the ideas are general enough to be applied for other density-based clustering algorithms and regression techniques, respectively.