Fuzzy Systems for Data Managing in Business, Society, and Economics

Dear Collegues,

Today, the volume of data digitally stored has considerably increased in the different ambits of society and business. This kind of data often involves noise, uncertainty, and vagueness. In such domains, efficient handling is of extreme importance with such fuzziness of data, without neglecting other features that are important, like the volume of data to be processed, their complexity, heterogeneity, etc.

Over the last few decades, fuzzy systems (FS) have gained attention in different domains, including business, economics, social media, medicine, etc., as both a theoretical framework for the representation of fuzziness and a new solution for managing and solving a great myriad of problems. Conventional theories have serious limitations that handling with different types of fuzziness in collected data may contain. FS has established a new research direction as well as a wide variety of methodologies and approaches that help to analyze large volumes of data. FS can handle not only expert knowledge, offering a closer representation to human thinking, but also automatically extracted knowledge from different devices such as sensors or sources, e.g., text in social media, or time series in the economic field.

Fuzzy systems open the way for new developments and methodologies. For instance, in the ambit of data mining and machine learning, different intelligent analysis techniques have been developed, experiencing a considerable growth in recent years due to two key factors: (a) Knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making in the current ultra-competitive markets; (b) the large volume of data managed by organizations makes it impossible to carry out an analysis process manually. Moreover, the volume of information also digitally stored in text format in open sources such as the web, including log files registering the use of information or social media content, has contributed to increasing the interest on text and web mining techniques. On one hand, these techniques aim to automatize the analysis process by introducing a variety of intelligent techniques to learn, optimize, and represent uncertain and imprecise knowledge. On the other hand, these tools offer the possibility to analyze massive data, offering more efficient algorithms and a suitable selection of obtained results in terms of their novelty, usefulness, and interpretability.

This Special Issue is dedicated to high-quality research works and solutions proposing original FS applications in business, society, and economy, addressing theoretical and/or practical problems employing a solid theory basis and/or empirical analysis.

Prof. Dr. Luis Terán
Dr. María Dolores Ruiz
Prof. Dr. Nadezhda Yarushkina
Dr. Rajkumar Verma
Guest Editors

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• Imprecision, uncertainty, and vagueness management in FS
• Data, text, web, and stream mining
• Temporal data series in FS
• Big data in FS
• Data pre- and post-processing in FS
• Parallel and distributed algorithms in FS
• Information summarization and visualization in FS
• Information measures in FS
• Information aggregation methods in FS
• Semantic models to represent input data and extracted knowledge in FS
• Applications of FS in several ambits: security, economy, health, tourism, biological process, customer profiles, insurance, decision making, expert systems, finance, anomaly detection, medical diagnosis, emergency management, situation recognition, pattern recognition, etc.