@article{gosain_performance_2016,
	series = {Proceedings of {International} {Conference} on {Communication}, {Computing} and {Virtualization} ({ICCCV}) 2016},
	title = {Performance {Analysis} of {Various} {Fuzzy} {Clustering} {Algorithms}: {A} {Review}},
	volume = {79},
	issn = {1877-0509},
	shorttitle = {Performance {Analysis} of {Various} {Fuzzy} {Clustering} {Algorithms}},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050916001459},
	doi = {10.1016/j.procs.2016.03.014},
	abstract = {Fuzzy clustering is useful clustering technique which partitions the data set in fuzzy partitions and this technique is applicable in many technical applications like crime hot spot detection, tissue differentiation in medical images, software quality prediction etc. In this review paper, we have done a comprehensive study and experimental analysis of the performance of all major fuzzy clustering algorithms named: FCM, PCM, PFCM, FCM-σ, T2FCM, KT2FCM, IFCM, KIFCM, IFCM-σ, KIFCM-σ, NC, CFCM, DOFCM. To better analysis their performance we experimented with standard data points in the presents of noise and outlier. This paper will act as a catalyst in the initial study for all those researchers who directly or indirectly deal with fuzzy clustering in their research work and ease them to pick a specific method as per the suitability to their working environment.},
	language = {en},
	urldate = {2021-10-01},
	journal = {Procedia Computer Science},
	author = {Gosain, Anjana and Dahiya, Sonika},
	month = jan,
	year = {2016},
	keywords = {CFCM, DOFCM, FCM, FCM-σ, Fuzzy Clustering, IFCM, IFCM-σ, Intuitionisitic, KFCM-σ, NC, PCM, Type 2},
	pages = {100--111},
}

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