Clustering without prior knowledge based on gene expression programming

Most existing clustering methods require prior knowledge, such as the number of clusters and thresholds. They are difficult to determine accurately in practice. To solve the problem, this study proposes a novel clustering algorithm named GEP-Cluster based on Gene Expression Programming (GEP) without prior knowledge. The main contributions include: (1) a new concept named Clustering Algebra is proposed that makes clustering as algebraic operation, (2) a GEP-Cluster algorithm is proposed to find the best clustering information automatic by GEP and discover the best clustering solution without any prior knowledge, (3) an AMCA (Automatic Merging Cluster Algorithm) algorithm is proposed to merge clustering automatically. Extensive experiments demonstrate that GEP-Cluster algorithm is effective in clustering without any prior knowledge on various data sets.