Data Scientist


Scientific Applications of Magnum Opus

In addition to its many commercial users, Magnum Opus is used for association discovery by scientists in a range of disciplines.  The following papers describe some scientific applications of Magnum Opus.

  1. Chris Ackermann, Rance Cleaveland, Samuel Huang, Arnab Ray, Charles Shelton and Elizabeth Latronico (2010) Automatic Requirement Extraction from Test Cases. In H. Barringer, Y. Falcone, B. Finkbeiner, K. Havelund, I. Lee, G. Pace, G. Rosu, O. Sokolsky and N. Tillmann (Eds.) Runtime Verification, Springer, Berlin, pp. 1-15.
  2. Irena I. Artamonova, Goar Frishman, Mikhail S. Gelfand, and Dmitrij Frishman (2005) Mining sequence annotation databanks for association patterns. Bioinformatics, 21: 49-57.
  3. Bartholomeusz, S. Locarnini, L. Yuen, A. Ayres and M. Littlejohn (2007) Multidrug Resistance and Cross-Resistance Pathways in HBV as a Consequence of Treatment Failure. Journal of Hepatology 46: S192.
  4. C.B. Baruque, L.B. Baruque and R.N. Melo (2006) Using Data Mining for the Refresh of Learning Objects Digital Libraries.  In Proceedings of the International Conference on Engineering Education, ICEE-2006.
  5. C.B. Baruque, M.A. Amaral, A. Barcellos, J.C. da Silva Freitas and C.J. Longo (2007) Analysing users’ access logs in Moodle to improve e learning. In Proceedings of the 2007 ACM Euro American conference on Telematics and information systems. pp. 1-4.
  6. Bhosale (2006) AlcoZone: An Adaptive Hypermedia Based Personalized Alcohol Education.  Masters Thesis.  Virginia Polytechnic Institute and State University.
  7. Jie Chen, Hongxing He, Huidong Jin, Damien McAullay, Graham Williams, and Chris Kelman, (2006) Identifying Risk Groups Associated with Colorectal Cancer, Lecture Notes in Computer Science, Volume 3755, Pages 260 – 272.
  8. Damaševičius (2009)  Analysis of Academic Results for Informatics Course Improvement Using Association Rule Mining.  In G.A. Papadopoulos et al. (eds.), Information Systems Development, Springer, Berlin, pp 357-363.
  9. George Dimitoglou and Shmuel Rotenstreich (2007) A System for Association Rule Discovery in Emergency Response Data.  In Sobh, Tarek (Ed.) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering.  Springer.  Pages 193-199.
  10. Daniel Druckman, Richard Harris, and Johannes Fürnkranz (2006) Modeling International Negotiation: Statistical and Machine Learning Approaches. In Robert Trappl (Ed.) Modeling International Negotiation Statistical and Machine Learning Approaches. Springer Netherlands, pp. 227-250.
  11. P. Early and C. E. Brodley (2005) Behavioral features for network anomaly detection, In M.A. Maloof (Ed.)  Machine Learning and Data Mining for Computer Security: Methods and Applications, pp. 107-124, Springer.
  12. Magdalini Eirinaki, Michalis Vazirgiannis, Iraklis Varlamis: (2003) SEWeP: using site semantics and a taxonomy to enhance the Web personalization process. KDD 2003: 99-108.
  13. Elstner, F. Stockhammer, T-N. Nguyen-Dobinsky, Q.L. Nguyen, I. Pilgermann, A. Gill, A. Guhr, T. Zhang, K. von Eckardstein, T. Picht, J. Veelken, R. L. Martuza, A. von Deimling, & A. Kurtz (2010) Identification of diagnostic serum protein profiles of glioblastoma patients. Journal of Neuro-Oncology.
  14. Georgii, L. Richter, U. Ruckert, and S. Kramer (2005) Analyzing microarray data using quantitative association rules. Bioinformatics 21: 123-129.
  15. Thomas Hellström (2003) Learning Robotic Behaviors with Association RulesWSEAS Transactions on Systems. Editor: Nikos Mastorakis. ISBN 1109-2777.
  16. Jianxin Jiao, Shaligram Pokharel, Lianfeng Zhang, and Yiyang Zhang (2005) Coordination of product and process variety in mass customization with data mining approachProceedings of the 10th Annual International Conference on Industrial Engineering – Theory, Applications and Practice.  Pages 342-348.
  17. Jianxin Jiao and Yiyang Zhang: (2005) Product portfolio identification based on association rule mining. Computer-Aided Design 37(2): 149-172
  18. Jianxin Jiao, Yiyang Zhang, and Martin Helander (2006) A Kansei mining system for affective design. Expert Systems with Applications, 30(4): 658-673.
  19. Jianxin Jiao, Yiyang Zhang, and Martin Helander (2006) Analytical Customer Requirement Analysis Based on Data MiningIn Voges, K. and Pope, N. (Eds.), Business Applications and Computational Intelligence, Idea Group, Hershey, ISBN: 1-59140-702-8, Chapter XII, pp. 227-247.
  20. Jianxin Jiao, L. Zhang, Y. Zhang, and S. Pokharel (2007) Association rule mining for product and process variety mapping. International Journal of Computer Integrated Manufacturing, 21(1): 111-124.
  21. Jianxin Jiao, Q.L. Zu, and Martin Helander (2007) Analytical modeling and evaluation of customer citarasa in vehicle designIEEE International Conference on Industrial Engineering and Engineering Management. Pages 1277-1281.
  22. Roger J. Jiao, Qianli Xu, Jun Du, Yiyang Zhang, Martin Helander, Halimahtun M. Khalid, Petri Helo and Cheng Ni (2007) Analytical Affective Design With Ambient Intelligence For Mass Customization And Personalization. International Journal of Flexible Manufacturing Systems, 19(4): 570-595
  23. Damien McAullay, Graham J. Williams, Jie Chen, and Huidong Jin: (2005) A Delivery Framework for Health Data Mining and Analytics. Australian Computer Science Conference 2005: 381-390.
  24. Mennis (2006) Socioeconomic-Vegetation Relationships in Urban, Residential Land: The Case of Denver, Colorado, Photogrammetric Engineering and Remote Sensing, 72(8):933.
  25. Mennis and J.W. Liu, (2005) Mining association rules in spatio-temporal data: an analysis of urban socioeconomic and land cover change. Transactions in GIS, 9(1): 13-18.
  26. K. Moon, S. R. T. Kumara, and T. W. Simpson, (2006) Data Mining and Fuzzy Clustering to Support Product Family Design. Proceedings of DETC06, 2006 ASME Design Engineering Technical Conferences, Philadelphia, PA, Paper No. DETC2006/DAC-99287.
  27. S.K. Moon, T.W. Simpson, & S.R.T. Kumara (2010) A methodology for knowledge discovery to support product family design. Annals of Operations Research, 174(1): 201-218.
  28. H.K. Nehemiah and A.Kannan (2006) A Diagnostic Decision Support System For Adverse Drug Reaction Using Temporal Reasoning. The International Journal of Artificial Intelligence and Machine Learning. 6(2): 79-86.
  29. Joseph Papaparaskevas, Yannis Batistakis, Maria Halkidi, Christos Amanatidis, Maria Kanellopoulou, Michalis Vazirgiannis, Evangelos Papafragas, and Alkiviadis C. Vatopoulos (2001) The use of Data Mining Techniques in Antibiotic Resistance Surveillance, Technical Report, Department of Informatics, Athens University of Economics and Business.
  30. Orna Raz (2004) Helping Everyday Users Find Anomalies in Data Feeds, Ph.D. Thesis – Software Engineering, Carnegie-Mellon University.
  31. K.K.W. Siu, S.M. Butler, T. Beveridge, J.E. Gillam, C.J. Hall, A.H. Kaye, R.A. Lewis, K. Mannan, G. McLoughlin, S. Pearson, A.R. Round, E. Schultke, G.I. Webb, and S.J. Wilkinson (2005). Identifying markers of pathology in SAXS data of malignant tissues of the brain. Nuclear Instruments and Methods in Physics Research A, 548:140-146. [Pre-publication PDF]
  32. Strand (2005) Controlling a Robot using Association Rules with a Temporal Component.  Masters Thesis.  Department of Computer Science, Umea University.
  33. Amalia Suzianti, Septy Apriliandary, Nabila Priscandy Poetri (2006)  Affective Design with Kansei Mining: An Empirical Study from Automotive Industry in Indonesia.  In A. Marcus (Ed.) Design, User Experience, and Usability: Novel User Experiences, Springer International Publishing, pp. 76-85.
  34. Tsironis, N. Bilalis, and V. Moustakis, (2005) Using machine learning to support quality management: Framework and experimental investigation. The TQM Magazine, 17(3): 237 – 248 .
  35. Srinivas Vinnakota and Nina S.N. Lam, (2006) Socioeconomic inequality of cancer mortality in the United States: A spatial data mining approach. International Journal of Health Geographics. 5: 9.
  36. Hei-Chia Wang, Yi-Shiun Lee, and Tian-Hsiang Huang (2006) Gene Relation Finding Through Mining Microarray Data and Literature.  In C. Priami et al. (Eds.): Transactions on Computational Systems Biology V, LNBI 4070, pp. 83–96.
  37. Daniel Wiechmann,  Understanding Complex Constructions: A Quantitative Corpus-Linguistic Approach to the Processing of English Relative Clauses. PhD dissertation: University of Jena.
  38. Linda L. Zhang (2012). Identifying mapping relationships between functions and technologies with association rule mining. International Journal of Computer Integrated Manufacturing, 25(6): 496-508.