Article

Adedoyin-Olowe, M., Gaber, M. and Stahl, F. (2013) TRCM: a methodology for temporal analysis of evolving concepts in Twitter. Proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing. (In Press)

Stahl, F. and Bramer, M. (2012) Scaling up classification rule induction through parallel processing. Knowledge Engineering Review. ISSN 1469-8005 doi: 10.1017/S0269888912000355

Stahl, F. and Bramer, M. (2012) Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge-Based Systems, 35. pp. 49-63. ISSN 0950-7051 doi: 10.1016/j.knosys.2012.04.014

Stahl, F. and Jordanov, I. (2012) An overview of the use of neural networks for data mining tasks. WIREs: Data Mining and Knowledge Discovery, 2 (3). pp. 193-208. ISSN 1942-4795 doi: 10.1002/widm.1052

Stahl, F. and Bramer, M. (2012) Jmax-pruning: a facility for the information theoretic pruning of modular classification rules. Knowledge-Based Systems, 29. pp. 12-19. ISSN 0950-7051 doi: 10.1016/j.knosys.2011.06.016

Swain, M., Silva, C. G., Loureiro-Ferreira, N., Ostropytskyy, V., Brito, J., Riche, O., Stahl, F., Dubitzky, W. and Brito, R. M. M. (2010) P-found: grid-enabling distributed repositories of protein folding and unfolding simulations for data mining. Future Generation Computer Systems, 26 (3). pp. 424-433. ISSN 0167-739X doi: 10.1016/j.future.2009.08.008

Berrar, D., Stahl, F., Silva, C., Rui Rodrigues, J., Brito, R. M. M. and Dubitzky, W. (2005) Towards data warehousing and mining of protein unfolding simulation data. Journal of Clinical Monitoring and Computing, 19 (4-5). pp. 307-317. ISSN 1573-2614 doi: 10.1007/s10877-005-0676-z

Book or Report Section

Stahl, F., Gabber, M. M. and Max, B. (2013) Scaling up data mining techniques to large datasets using parallel and distributed processing. In: Rausch, P., Sheta, A. F. and Ayesh, A. (eds.) Business Intelligence and Performance Management. Springer, pp. 243-259. ISBN 9781447148654 doi: 10.1007/978-1-4471-4866-1_16

Stahl, F., May, D. and Bramer, M. (2012) Parallel random prism: a computationally efficient ensemble learner for classification. In: Bramer, M. and Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX. Springer, London, pp. 21-34. ISBN 9781447147381 doi: 10.1007/978-1-4471-4739-8_2 (Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence)

Stahl, F., Gaber, M. M., Aldridge, P., May, D., Liu, H., Bramer, M. and Yu, P. S. (2012) Homogeneous and heterogeneous distributed classification for pocket data mining. In: Hameurlain, A., Küng, J. and Wagner, R. (eds.) Transactions on large-scale data and knowledge-centered systems V. Lecture Notes in Computer Science (7100). Springer, pp. 183-205. ISBN 9783642281471

Stahl, F., Gaber, M. M. and Salvador, M. M. (2012) eRules: a modular adaptive classification rule learning algorithm for data streams. In: Bramer, M. and Petridis, M. (eds.) Research and Development in Intelligent Systems XXIX. Springer, London, pp. 65-78. ISBN 9781447147381 doi: 10.1007/978-1-4471-4739-8_5

Stahl, F., Gaber, M. M., Liu, H., Bramer, M. and Yu, P. S. (2011) Distributed classification for pocket data mining. In: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z. W. (eds.) Foundations of Intelligent Systems, Proc. of ISMIS 20111, the 19th Int. Symposium on Methodologies for Intelligent Systems. Lecture Notes in Computer Science (6804). Springer, pp. 336-345. ISBN 9783642219153 doi: 10.1007/978-3-642-21916-0_37

Stahl, F., Gaber, M. M., Bramer, M. and Yu, P. S. (2011) Distributed hoeffding trees for pocket data mining. In: International Conferance on High Performance Computing and Simulation (HPCS), 2011. IEEE, pp. 686-692. ISBN 9781612843803 doi: 10.1109/HPCSim.2011.5999893

Stahl, F. and Bramer, M. (2011) Induction of modular classification rules: using Jmax-pruning. In: Bramer, M., Petridis, M. and Hopgood, A. (eds.) Research and Development in Intelligent Systems XXVII. Springer, London, pp. 79-92. ISBN 9780857291295 doi: 10.1007/978-0-85729-130-1_6

Stahl, F. and Bramer, M. (2011) Random prism: an alternative to random forests. In: Bramer, M., Petridis, M. and Nolle, L. (eds.) Research and Development in Intelligent Systems XXVIII. Springer, London, pp. 5-18. ISBN 9781447123170 doi: 10.1007/978-1-4471-2318-7_1

Stahl, F., Gaber, M. M., Bramer, M. and Yu, P. S. (2010) Pocket data mining: towards collaborative data mining in mobile computing environments. In: Proc. ICTAI 2010, the 22nd IEEE Int. Conf. on Tools with Artificial Intelligence. IEEE, pp. 323-330. ISBN 9781424488179 doi: 10.1109/ICTAI.2010.118

Stahl, F., Bramer, M. and Adda, M. (2010) J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice III. IFIP Advances in Information and Communication Technology (331). Springer, Berlin, pp. 47-56. ISBN 9783642152856 doi: 10.1007/978-3-642-15286-3_5

Stahl, F., Bramer, M. and Adda, M. (2010) Parallel rule induction with information theoretic pre-pruning. In: Research and Development in Intelligent Systems XXVI. Springer, London, pp. 151-164. ISBN 9781848829824 doi: 10.1007/978-1-84882-983-1_11

Stahl, F., Bramer, M. and Adda, M. (2009) PMCRI: a parallel modular classification rule induction framework. In: Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science (5632). Springer, pp. 148-162. ISBN 9783642030697 doi: 10.1007/978-3-642-03070-3_12

Stahl, F., Bramer, M. and Adda, M. (2009) Parallel induction of modular classification rules. In: Bramer, M., Petridis, M. and Coenen, F. (eds.) Research and Development in Intelligent Systems XXV. London, Berlin, pp. 343-348. ISBN 9781848821705 doi: 10.1007/978-1-84882-171-2_25 (Proceedings of AI-2008, the Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence)

Swain, M., Ostropytskyy, V., Silva, C. G., Stahl, F., Riche, O., Brito, R. M. M. and Dubitzky, W. (2008) Grid computing solutions for distributed repositories of protein folding and unfolding simulations. In: Computational Science – ICCS 2008. Lecture Notes in Computer Science (5103). Springer, Berlin, pp. 70-79. ISBN 9783540693888 doi: 10.1007/978-3-540-69389-5_10

Stahl, F., Bramer, M. and Adda, M. (2008) P-Prism: a computationally efficient approach to scaling up classification rule induction. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP – The International Federation for Information Processing (276). Springer, USA, pp. 77-86. ISBN 9780387096940 doi: 10.1007/978-0-387-09695-7_8

Stahl, F. and Bramer, M. (2008) Towards a computationally efficient approach to modular classification rule induction. In: Bramer, M., Coenen, F. and Petridis, M. (eds.) Research and Development in Intelligent Systems XXIV. Springer, London, pp. 357-362. ISBN 9781848000933 doi: 10.1007/978-1-84800-094-0_27

Stahl, F., Berrar, D., Silva, C., Rodrigues, R., Brito, R.M.M. and Dubitzky, W. (2005) Grid warehousing of molecular dynamics protein unfolding data. In: Proceedings of CCGrid2005, the IEEE 2005 International Symposium on Cluster Computing and the Grid. IEEE, pp. 496-503. ISBN 0780390741 doi: 10.1109/CCGRID.2005.1558594