A new weighted fuzzy c-means based on the collective behaviour of starling birds

In this paper, a new weighted fuzzy c-means clustering algorithm is proposed. The presented approach consists of emulating the collective behaviour of starling birds to form homogeneous and well-separated clusters. In a flock of starlings, each individual maintains a connection with its neighborhood to determine its position in space. This connection allows the individual to approach the flock-mates that have the same direction as its own, and simultaneously, to move away from other individuals that have a different direction. Based on this metaphor, in this work, we propose the use of the three elementary movements of the starling bird, separation, alignment, and cohesion, to update the weight parameter associated with each individual in the dataset. The accuracy of the proposed algorithm has been assessed by two clustering validation indices. Compared to some existing algorithms, our algorithm provides better results. An example of artificial data is used to contrast some cases of this approach.