Robust clustering of imprecise data

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

“…Apart from that, it could also be interesting to investigate the application of the DOS-FCM in clustering the stationary and non-stationary time series data [52][53][54][55] and anomalous data [56].…”

Fuzzy Rule Learning for Material Classification from Imprecise Data

“…Distances or dissimilarities have already been defined and used with imprecise or uncertain data. A distance consisting in the sum of the center Euclidian distance and the spread Euclidian distance of the imprecise data was used in [5] and [7] but this implies a loss of the information provided by the whole distribution. Moreover, even though it might have been replaced by the deviation distance, the spread distance does not make a lot of sense in the case of normal distributions whose spreads are theoretically infinite, and in our case imposed by the domain boundaries.…”

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

“…Suggestive applications of fuzzy clustering methods for fuzzy data in e-health and tourism have been suggested, respectively, by D'Urso et al [74] and D'Urso et al [76,78] . Recently, robust fuzzy clustering methods for fuzzy data have been suggested by D'Urso and De Giovanni [77] . They, using a "Partitioning Around Medoids" (PAM) approach, firstly proposed a timid robustification of the fuzzy clustering for LR fuzzy data; successively, proposed three robust fuzzy clustering models based, respectively, on noise cluster, exponential metric and trimming rules.…”