Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose /FPF-SB/, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the /k/-center problem and a stability-based method for determining the number of clusters /k/. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality
