A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality

Thong, Pham Huy; Sơn, Lê Hoàng

doi:10.1016/j.knosys.2016.06.023

Cited by 106 publications

(34 citation statements)

References 23 publications

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“…N. T. Thong and Son (2015) proposed the model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. P. H. Thong and Son (2016) proposed the Automatic Picture Fuzzy Clustering (AFC-PFS) for determining the most suitable number of clusters for FC-PFS. G. W. Wei (2016) proposed the multiple attribute decision making (MADM) method based on the proposed picture fuzzy cross entropy.…”

Section: Introductionmentioning

confidence: 99%

Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its Application to Green Suppliers Selections

Zhang

Wei

Gao

et al. 2019

Technological and Economic Development of Economy

105

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In this paper, we construct picture fuzzy EDAS model based on traditional EDAS (Evaluation based on Distance from Average Solution) model. Firstly, we briefly review the definition of picture fuzzy sets (PFSs) and introduce the score function, accuracy function and operational laws of picture fuzzy numbers (PFNs). Then, we combine traditional EDAS model for MCGDM with PFNs. In our model, it’s more accuracy and effective for considering the conflicting attributes. Finally, a numerical example for green supplier selection has been given to illustrate this new model and some comparisons between EDAS model with PFNs and PFWA, PFWG aggregation operators are also conducted to further illustrate advantages of the new method.

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Section: Introductionmentioning

confidence: 99%

Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its Application to Green Suppliers Selections

Zhang

Wei

Gao

et al. 2019

Technological and Economic Development of Economy

105

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“…Additionally, PFSs can precisely describe a DMs' opinions, including yes, abstain, no, and refusal, that can avoid any missing information required for evaluation purposes and make data to be more reliable and adaptable with an actual decision-making environment than IFSs. Recently, PFSs-based studies have focused on their extensions and decisionmaking methods to deal with various MCDM and clustering analysis problems (Singh, 2015;Wei, 2016;Thong, 2016aThong, , 2016bSon, 2017;Zhang, Wang, & Hu, 2018;L. Wang, Peng, & J. Q. Wang, 2018;L.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Picture Fuzzy Anp-Todim Multi-Criteria Decision-Making Approach for Tourism Attraction Recommendation

Tian

Peng

2019

Technological and Economic Development of Economy

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In this paper, the picture fuzzy score and accuracy function are first defined. Then, a corresponding comparative method between two picture fuzzy numbers (PFNs) is developed. Next, a novel normalized picture fuzzy distance measure between two PFNs is disclosed, and part of the characteristics of the proposed distance measure are discussed. Afterwards, on the basis of the analytic network process (ANP) and an Acronym in Portuguese of Interactive and Multi-Criteria Decision-Making (TODIM) methods, an integrated ANP-TODIM approach is developed to resolve multi-criteria decision-making (MCDM) where the weights of the criteria are fully unknown. We use ANP approach to decide the weights of criteria on the basis of expert mean assessment method, and TODIM is utilized to obtain the ranking of alternatives. Finally, an illustrative example of an optimal tourism attraction recommendation is provided to testify applicability of the developed decision-making method and prove that its results are effective and reasonable.

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“…In [10], a picture fuzzy clustering algorithm, called FC-PFS, was proposed. In order to determine the number of clusters, they built an automatically determined most suitable number of clusters based on particle swarm optimization and picture composite cardinality for a dataset [23]. They also extended the picture fuzzy clustering algorithm for complex data [24].…”

Section: Introductionmentioning

confidence: 99%

Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

Smarandache

et al. 2018

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Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.

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A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality

Cited by 106 publications

References 23 publications

Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its Application to Green Suppliers Selections

Edas Method for Multiple Criteria Group Decision Making With Picture Fuzzy Information and Its Application to Green Suppliers Selections

An Integrated Picture Fuzzy Anp-Todim Multi-Criteria Decision-Making Approach for Tourism Attraction Recommendation

Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

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