The concept of intuitionistic fuzzy set (IFS) was originally introduced by Atanassov (1983) to extend the concept of the traditional fuzzy set. Each element in an IFS is expressed by an ordered pair which is called an intuitionistic fuzzy value (IFV) (or intuitionistic fuzzy number (IFN)), and each IFV is characterized by a membership degree, a nonmembership degree, and a hesitancy degree. The sum of the membership degree, the nonmembership degree, and the hesitancy degree of each IFV is equal to one. IFVs can describe the fuzzy characters of things comprehensively, and thus are a powerful and effective tool in expressing uncertain or fuzzy information in actual applications. Recently, a lot of research work has been done on the aggregation and cluster analysis. Since 2006, my research group has been focusing on the investigation of these interesting and important topics, and achieved fruitful research results which have been published in some well-known peer-reviewed professional journals.

This book offers a systematic introduction to the latest research work of my group on information aggregation and cluster analysis under intuitionistic fuzzy environments, including the various algorithms for clustering intuitionistic fuzzy information and the intuitionistic fuzzy aggregation techniques, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, and so on.