@inproceedings{16a5a59efa9b440798725c6bc725e443,
title = "On the design of similarity measures based on fuzzy integral",
abstract = "Similarity measure for fuzzy sets is designed with the help of a conventional fuzzy measure and integral. Similarity measure based on fuzzy integral not only evaluates similarity but also captures the characteristics occurring between various data sets. Compared to a conventional approach based on a distance measure, the proposed similarity measure based on fuzzy integral delivers additional information that convergence in similarity value provides data comparison structure between data sets. The properties of the proposed similarity measure are analyzed and demonstrated with illustrative examples. The degree of each data set and its distribution plays a crucial role in discriminating data characteristics. The designed similarity measure shows its convergence. Comparison with random data is carried out, and its similarity value and convergence properties are analyzed with the use of the similarity measure.",
keywords = "convergence, fuzzy integral, fuzzy measure, similarity measure",
author = "Jaehoon Cha and Sanghyuk Lee and Kim, {Kyeong Soo} and Witold Pedrycz",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017 ; Conference date: 27-06-2017 Through 30-06-2017",
year = "2017",
month = aug,
day = "30",
doi = "10.1109/IFSA-SCIS.2017.8023367",
language = "English",
series = "IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems",
}