Journal
APPLIED SOFT COMPUTING
Volume 82, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105572
Keywords
Probabilistic linguistic term set; Information measure; Relationships; Clustering algorithm; Classifying cities
Categories
Funding
- National Natural Science Foundation of China [71571156]
- 2019 Sichuan Planning Project of Social Science, China [SC18A007]
- 2018 Key Research Institute of Humanities and Social Sciences in Sichuan Province, China [Xq18A01]
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The probabilistic linguistic term set is a powerful tool to express and characterize people's cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information measures such as the distance measure, similarity measure, entropy measure and correlation measure should be defined. However, as an important kind of information measure, the inclusion measure has not been defined by scholars. This study aims to propose the inclusion measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and entropy measures of probabilistic linguistic term sets to construct a unified framework of information measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, entropy and inclusion measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China. (C) 2019 Elsevier B.V. All rights reserved.
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