Anomaly/outlier detection using fuzzy clustering

I understand that fuzzy clustering using FCM produces a membership matrix for the set of data points we feed to it. What characteristics will an anomalous cluster produced during this method have? (Considering I only have unlabelled data)

As far as I know there is no unique definition for what is an outlier/anomaly. Therefore you'll have to decide by yourself what the characteristics of outlying data points in your data set are. This could be for example: distance to the cluster center (threshold), local neighbourhood (a data point that has no/few data points in its neighgbourhood might be an outlier) or statistical characteristics (an outlier doesn't 'fit' to the distribution of a cluster, e.g. Grubbs' method). You can find a survey of outlier detection methods in A Survey of Outlier Detection Methodologies by Hodge and Austin (2004).

StackExchange Computer Science Q#13481, answer score: 3