Application of Bio-inspired Methods Within Cluster Forest Algorithm

Cluster Forest (CF) is relatively new ensemble clustering method inspired by Random Forest algorithm. The main idea behind of the existing algorithm consists in a construction of a larger number of partial clusterings for feature subsets using K-means algorithm. At the end, these clusterings are aggregated using a method of spectral clustering. This article describes a new application of bio-inspired methods that replaces the K-means algorithm in the computation pipeline. Several bio-inspired methods were tested on eight different datasets and compared with the original CF and others well known clustering methods.