Knowledge discovery in data sets with graded attributes

Abstract

We present a knowledge discovery method for graded attributes that is based on an interactive determination of implications (if-then-rules) holding between the attributes of a given data-set. The corresponding algorithm queries the user in an efficient way about implications between the attributes. The result of the process is a representative set of examples for the entire theory and a set of implications from which all implications that hold between the attributes can be deduced. In many instances, the exploration process may be shortened by the usage of the user’s background knowledge. That is, a set of of implications the user knows beforehand. The method was successfully applied in different real-life applications for discrete data. In this paper, we show that attribute exploration with background information can be generalized for graded attributes.

Keywords:

• Knowledge discovery
• formal concept analysis
• attribute exploration
• fuzzy data

Notes

No potential conflict of interest was reported by the author.

1 We used Conexp-clj for the computation of the L-concepts. The freeware is available at https://github.com/exot/conexp-clj.