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 clustering algorithm to choose. In this paper, this hesitation is modeled by a hesitant fuzzy multi criteria decision making problem {small (HFMCDM)} in which some clustering algorithms play the role of experts. Here, we consider fuzzy {footnotesize C}-means {small (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 {small (HFC)} to decide which of the {small FCM} family or hierarchical clustering algorithms is suitable for our data. This procedure ascertains a good clustering algorithm using neutrosophic {small FCM} ({small NFCM}) through a two phases process. The {small HFC} procedure not only makes a true decision about applying partitioning clustering algorithms, but also improves the performance of {small FCM} and evolutionary kernel intuitionistic fuzzy c-means clustering algorithm ({small EKIFCM}) with construction hesitant fuzzy partition {small (HFP)} conveniently. Experimental results show that the clustering procedure is applicable and practical. According to {small 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.