FPF-SB : A Scalable Algorithm for Microarray Gene Expression Data Clustering

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 kcenter 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.

• Type: Conference
• Year: 2007
• Where: HCI
• Authors: Filippo Geraci, Mauro Leoncini, Manuela Montangero, Marco Pellegrini, M. Elena Renda
• Keywords: Gene Expression Data, HCI 2007, Human Computer Interaction, Large Datasets, Microarray Gene Expression