Outlier detection using linguistically quantified statements

Abstract

Automatic summary of databases is an important tool in strategic decision-making. This paper applies the concept of linguistic summaries of databases to outlier detection. The definition of an outlier is closely related to the type of data analyzed and its context. Outlier detection is an important data-mining technique, which finds applications in a wide range of domains. It can identify defects, remove impurities from the data, and, most of all, it is significant to decision-making processes. The authors propose a novel definition of an outlier, based on linguistic quantifiers and linguistic summaries. Linguistic quantifiers are employed to express the cardinality of a set of outliers in a natural language. Thus, this paper demonstrates that linguistic summaries proposed by Yager, which provide the ability to model imprecise information, can serve as an effective tool for outlier detection.