HFC: Data clustering based on hesitant fuzzy decision making

In a clustering task, choosing a proper clustering algorithm and obtaining qualified clusters are crucial issues. Sometimes, a clustering algorithm is chosen based on the data distribution, but data distributions are not known beforehand in real world problems. In this case, we hesitate which cluste...

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Vydáno v:Iranian journal of fuzzy systems (Online) Ročník 19; číslo 5; s. 167
Hlavní autoři: Aliahmadipour, L, Eftekhari, M, Torra, V
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zahedan University of Sistan and Baluchestan, Iranian Journal of Fuzzy Systems 2022
Témata:
Algorithms
Cluster analysis
Clustering
Datasets
FCM clustering algorithm
Fuzzy sets
hesitant fuzzy clustering
hesitant fuzzy decision making
hierarchical clustering algorithm
Machine learning
Methods
Multiple criteria decision making
ISSN:1735-0654, 2676-4334, 2676-4334
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Shrnutí:In a clustering task, choosing a proper clustering algorithm and obtaining qualified clusters are crucial issues. Sometimes, a clustering algorithm is chosen based on the data distribution, but data distributions are not known beforehand in real world problems. In this case, we hesitate which clustering algorithm to choose. In this paper, this hesitation is modeled by a hesitant fuzzy multi criteria decision making problem (HFMCDM) in which some clustering algorithms play the role of experts. Here, we consider fuzzy C-means (FCM) and agglomerative clustering algorithms as representative of two popular categories of clustering algorithms partitioning and hierarchical clustering methods, respectively. Then, we propose a new clustering procedure based on hesitant fuzzy decision making approaches (HFC) to decide which of the FCM family or hierarchical clustering algorithms is suitable for our data. This procedure ascertains a good clustering algorithm using neutrosophic FCM (NFCM) through a two phases process. The HFC procedure not only makes a true decision about applying partitioning clustering algorithms, but also improves the performance of FCM and evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) with construction hesitant fuzzy partition (HFP) conveniently. Experimental results show that the clustering procedure is applicable and practical. According to HFC procedure, it should be mentioned that it is possible to replace the other clustering algorithms that belong to any partitioning and hierarchical clustering methods. Also, we can consider other categories of clustering algorithms.
Bibliografie:ObjectType-Article-1
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ISSN:1735-0654
2676-4334
2676-4334
DOI:10.22111/ijfs.2022.7163