Detection of outlier information by the use of linguistic summaries based on classic and interval-valued fuzzy sets

Abstract

Automatic summary of databases is an important tool in strategic decision-making. This paper presents the application of linguistic summaries to outlier detection in databases containing both text and numeric attributes. The proposed method applies Yager’s standard summary based on interval-valued fuzzy sets. Fuzzy similarity measures are the features which are looked for. Detection of outliers can identify defects, remove impurities from the data, and, most of all, it may provide the basis for decision-making processes. In this paper, we introduce a definition of an outlier based on linguistic summaries. Feasibility of the method is demonstrated on practical examples.