Int. J. Data Mining and Bioinformatics, Vol. 10, No. 1, 2014 83 Copyright © 2014 Inderscience Enterprises Ltd. Extracting a cancer model by enhanced ant colony optimisation algorithm Reza Shamsaee High Performance Computation Laboratory (HPC lab), School of Computer Engineering, Iran University of Science and Technology, (IUST), Tehran, Iran E-mail: r_shamsaee@iust.ac.ir and Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran Mahmood Fathy* High Performance Computation Laboratory (HPC lab), School of Computer Engineering, Iran University of Science and Technology, (IUST), Tehran, Iran E-mail: mahfathy@iust.ac.ir Ali Masoudi-Nejad* Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran E-mail: amasoudin@ut.ac.ir *Corresponding authors Abstract: Although Ant-Miner has been used with relative ease for datasets with categorical data and small-sized feature vectors, microarray datasets, which contain a few samples with large amount of genes, are a totally different story. The Ant-Miner is an ant colony optimisation algorithm that extracts predictive rules from datasets and intrinsically works on discrete values. This study has developed a new algorithm, “Enhanced Ant-Miner” (EAM), based on previous works. EAM deals with continuous attributes as well as categorical ones and presents its captured models in the form of predictive rules. EAM has been tested versus SVM, CN2, K-means and hierarchical clustering and the results show that EAM is the best in the context of predictive accuracy. Additionally, its agent-based nature gives it a much more charming ability to speed up the whole process when compared to other trivial miners. Keywords: mining; abstract model; ant colony optimisation and microarray; cancer model. Reference to this paper should be made as follows: Shamsaee, R., Fathy, M. and Masoudi-Nejad, A. (2014) ‘Extracting a cancer model by enhanced ant colony optimisation algorithm’, Int. J. Data Mining and Bioinformatics, Vol. 10, No. 1, pp.83–97.