| 1. |
- Aliahmadipour, Laya, et al.
(författare)
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A definition for hesitant fuzzy partitions
- 2016
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Ingår i: International Journal of Computational Intelligence Systems. - : Taylor & Francis Group. - 1875-6891 .- 1875-6883. ; 9:3, s. 497-505
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.
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| 2. |
- Aliahmadipour, Laya, et al.
(författare)
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On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
- 2017
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Ingår i: Fuzzy sets, rough sets, multisets and clustering. - Cham : Springer. - 9783319475561 - 9783319475578 ; , s. 157-168
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Bokkapitel (refereegranskat)abstract
- Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and initial parametrization of a clustering (classification) algorithm. Hesitant fuzzy set theory is a suitable tool to deal with this kind of problems. In this study, we introduce two different points of view to apply hesitant fuzzy sets in the data mining tasks, specially in the clustering algorithms.
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| 3. |
- Bozorgpanah, Aso, et al.
(författare)
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Privacy and explainability : the effects of data protection on shapley values
- 2022
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Ingår i: Technologies. - : MDPI. - 2227-7080. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- There is an increasing need to provide explainability for machine learning models. There are different alternatives to provide explainability, for example, local and global methods. One of the approaches is based on Shapley values. Privacy is another critical requirement when dealing with sensitive data. Data-driven machine learning models may lead to disclosure. Data privacy provides several methods for ensuring privacy. In this paper, we study how methods for explainability based on Shapley values are affected by privacy methods. We show that some degree of protection still permits to maintain the information of Shapley values for the four machine learning models studied. Experiments seem to indicate that among the four models, Shapley values of linear models are the most affected ones.
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| 4. |
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| 5. |
- Torra, Vicenç, et al.
(författare)
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Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters
- 2016
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Ingår i: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). - : IEEE. - 9781509006267 - 9781509006250 ; , s. 524-530
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Konferensbidrag (refereegranskat)abstract
- In this paper we discuss how three types of fuzzy partitions can be used to describe the results of three types of cluster structures. Standard fuzzy partitions are suitable for centroid based clusters, and I-fuzzy partitions for clusters represented by segments or lines (e.g., c-varieties). In this paper, we introduce hesitant fuzzy partitions. They are suitable for clusters defined by sets of centroids. Because of that, we show that they are useful for hierarchical clustering. We also establish the relationship between hesitant fuzzy partitions and I-fuzzy partitions.
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