A fuzzy incremental clustering approach to hybrid data discovery
Radu Găceanu, Horia F. Pop · Acta Electrotechnica et Informatica · 2012
We propose an incremental fuzzy clustering algorithm for hybrid data discovery.The algorithm is based on the ASM model where data items are represented by agents placed in a two dimensional grid.The agents will group themselves into clusters by making simple moves in their environment.They will try to get closer to each other if they are rather similar or to get away from each other if they are rather different.The algorithm allocates a new agent on the grid whenever a new data item arrives.At each step the new agent contacts an agent from the grid and if they are similar then they will group together in the same cluster.Whenever a new cluster is created the agents will try to merge the cluster with one of the previously created clusters.If a newly created agent does not find a similar fellow then it will start an ASM-like process in order to search for one and thus the data is clustered.