On LAMDA clustering method based on typicality degree and intuitionistic fuzzy sets

作者：

Highlights：

• The paper proposes GTD and IGAD functions to improve the LAMDA algorithm.

• GTD and IGAD functions are mixed by an interpolation function called TIGAD.

• Methods 6 and 7 generated the best results for study cases.

• Methods 6 and 7 generated a good balance of the number of classes and PE values.

• The proposed method improved the classes generation and the quality of clustering.

摘要

•The paper proposes GTD and IGAD functions to improve the LAMDA algorithm.•GTD and IGAD functions are mixed by an interpolation function called TIGAD.•Methods 6 and 7 generated the best results for study cases.•Methods 6 and 7 generated a good balance of the number of classes and PE values.•The proposed method improved the classes generation and the quality of clustering.

论文关键词：LAMDA,Fuzzy clustering,Typicality function,Membership function,Reinforcement operator

论文评审过程：Received 9 June 2017, Revised 16 April 2018, Accepted 17 April 2018, Available online 18 April 2018, Version of Record 3 May 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.04.022