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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.