Evolving Clustering via the Dynamic Data Assigning Assessment Algorithm
Olga Georgieva, Frank Klawonn · 2006
Following the idea to search for just one cluster at a time a prototype-based clustering algorithm named dynamic data assigning assessment (DDAA) was recently proposed. It is based on the noise clustering technique and finds single good clusters one by one and at the same time it separates the noise data. In this paper we present the basic idea and executive procedures of evolving variant of DDAA algorithm that are capable to deal with the currently entered system information. The evolving DDAA algorithm assigns every new data point to an already determined good cluster or, alternatively, to the noise cluster. It checks whether the new data collection provides a new good cluster(s) and thus, changes the data structure. The assignment could be done in hard or fuzzy sense