Ansari, "A position-based clustering technique for Ad Hoc Intervehicle communication," IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, vol.
Valaee, "Mobility-based clustering in VANETs using Affinity Propagation," in Proc.
However, this scheme incurs more overhead and memory consumption when compared to hard clustering approaches.
The concept of rough set theory developed by a polish scientist Pawlak [19] is a method for making the decision in case of uncertainty or vagueness in clustering. A data set is represented as an information table I = (U, A) where U represents each row of objects and 'A' represents each column of attributes.
Buttyan, "Secure and Reliable
Clustering in Wireless Sensor Networks: A Critical Survey," Computer Networks, vol.
When compared with traditional single
clustering algorithms, cluster ensemble approaches are able to integrate multiple
clustering solutions obtained from different data sources into a unified solution and provide a more robust, stable and accurate final result.
The K-Means cluster can be affected by the initial
clustering centers, and the typical K-Means cluster results (K = 11) are shown in Figure 7(a).
The error percentage criterion was used to compare
clustering results of both algorithms.
In the FCM experiment in this paper given gray values of the pixel which in the repaired areas, we can calculate the Euclidean distance of it with each
clustering center concluded by formula (8), the cluster which has the minimum distance is the repaired area D.