Dmitriy's Fradkin Publications

Patents Granted

  • Dmitriy Fradkin, Fabian Moerchen.  Condition monitoring with automatically generated error templates from log messages and sensor trends based on time semi-intervalsUS 8423493.
  • Razvan Ioan Ionasec, Puneet Sharma, Bogdan Georgescu, Andrey Torzhkov, Fabian Moerchen, Gayle M. Wittenberg, Dmitriy Fradkin, Dorin Comaniciu. Multi-Component Heart and Aorta Modelling from High-Resolution MR and CT for Decision Support in Cardiac DiseaseUS 8527251.

  • Dmitriy Fradkin, Fabian Moerchen. Temporal pattern matching in large collections of log messages. US 9026550



Publications  (in reverse chronological order)

  1. Incorporating Task Analysis in the Design of a Tool for a Complex and Exploratory Search Task. Tugba Kulahcioglu, Dmitriy Fradkin, Sridharan Palanivelu. ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), March 2017. link
  2. An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data. Iyad Batal, Gregory Cooper,Dmitriy Fradkin, James Harrison, Fabian Moerchen, Milos Hauskrecht. Knowledge and Information Systems, 2016. link
  3. Mining Sequential Patterns for Classification. Dmitriy Fradkin, Fabian Moerchen. Knowledge and Information Systems, 2015. link pdf appendix 
  4. Log-based predictive maintenanceRuben Sipos, Dmitriy Fradkin, Fabian Moerchen, Zhuang Wang.  International Conference on Knowledge Discovery and Data Mining (KDD), August 2014 link
  5. Atlas of the clinical genetics of human dilated cardiomyopathy. J Haas et. al. European heart journal, 2014. link
  6. Mining compressing sequential patterns. Hoang Thanh Lam, Fabian Moerchen, Dmitriy Fradkin, Toon Calders. Statistical Analysis and Data Mining: The ASA Data Science Journal 7 (1), 34-52, 2014 link
  7. Zips: Mining compressing Sequential Patterns in Streams. Hoang Thanh Lam, Toon Calders, Jie Yang, Fabian Moerchen and Dmitriy Fradkin. Interactive Data Exploration and Analytics (IDEA) workshop at International Conference on Knowledge Discovery and Data Mining (KDD), August 2013. link pdf 
  8. An Efficient Approach for Mining Recent Temporal Patterns. Iyad Batal, Dmitriy Fradkin,James Harrison, Fabian Moerchen, Milos Hauskrecht. International Conference on Knowledge Discovery and Data Mining (KDD), August 2012. link pdf 
  9. Mining Compressing Sequential Patterns. Hoang Thanh Lam, Fabian Moerchen, Dmitriy Fradkin, Toon Calders. SIAM Conference on Data Mining (SDM), April 2012 link pdf 
  10. Single Pass Text Classification by Direct Feature Weighting. H.H.Malik, D. Fradkin, F. Mörchen. Knowledge and Information Systems, 2011 link 
  11. Margin-Closed Frequent Sequential Pattern Mining. D. Fradkin, F. Mörchen, F. In Proceedings of Useful Patterns Workshop (UP), KDD'10 link pdf 
  12. Hierarchical document clustering using local patterns. H.H.Malik, J.R Kender, D. Fradkin, F. Mörchen. Data Mining and Knowledge Discovery 21(1), 2010 Springer link 
  13. Robust mining of time intervals with semi-interval partial order patterns. F. Mörchen, D. Fradkin. In Proceedings SIAM Conference on Data Mining (SDM), 2010, pp. 315-326 link pdf 
  14. Emerging Trend Prediction in Biomedical Literature. F. Moerchen, D. Fradkin, M. Dejori, B. Wachmann. Proceedings of AMIA Annual Symposium 2008: 485-489, November 2008 pdf 
  15. Classifying Spend Transactions with Off-the-Shelf Learning Components. Saikat Mukherjee, Dmitriy Fradkin, Michael Roth.  Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). November 2008. pdf   Presentation
  16. Clustering Inside Classes Improves Performance of Linear Classifiers. Dmitriy Fradkin.  Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). November 2008: pdf   Long version: pdf   Presentation
  17. Anticipating Annotations and Emerging Trends in Biomedical Literature. F. Mörchen, M. Dejori, D. Fradkin, J. Etienne, B. Wachmann, M. Bundschus. Proceedings of KDD'08. link pdf  Presentation
  18. Validation of Epidemiological Models: Chicken Epidemiology in the UK. Dmitriy Fradkin, Ilya Muchnik, Patrick Hermans, Kenton Morgan. Eds. J. Abello and G. Carmode. "Discrete Methods in Epidemiology", DIMACS Series in Discrete Mathematics and Theoretical Computer Science, volume 70, pp. 243-256, 2006. pdf
  19. Support Vector Machines. Dmitriy Fradkin, Ilya Muchnik. Eds. J. Abello and G. Carmode. "Discrete Methods in Epidemiology", DIMACS Series in Discrete Mathematics and Theoretical Computer Science, volume 70, pp. 13-20, 2006. pdf
  20. Prevalence of wet litter and the associated risk factors in broiler flocks in the United Kingdom. P. G. Hermans, D. Fradkin, I. B. Muchnik, and K. L. Morgan.  Veterinary Record, May 2006 (158):615 - 622.
  21. Simulated Entity Resolution by Diverse Means: DIMACS Work on the KDD Challenge of 2005. Andrei Anghelescu, Aynur Dayanik, Dmitriy Fradkin, Alex Genkin, Paul Kantor, David Lewis, David Madigan, Ilya Muchnik and Fred Roberts.  DIMACS Technical Report 2005-42: ps.gz 
  22. Machine Learning Methods in the Analysis of Lung Cancer Survival Data. Dmitriy Fradkin, Dona Schneider and Ilya Muchnik. DIMACS Technical Report 2005-35: ps.gz 
  23. Bayesian Multinomial Logistic Regression for Author identification. David Madigan, Alexander Genkin, David D. Lewis and Dmitriy Fradkin. MaxEnt 2005: pdf
  24. Author Identification on the Large Scale. David Madigan, Alexander Genkin, David D. Lewis, Shlomo Argamon, Dmitriy Fradkin and Li Ye.  Classification Society of North America: CSNA'05 pdf 
  25. Methods for Learning Classifier Combinations: No Clear Winner. Dmitriy Fradkin and Paul Kantor. Proceedings of ACM Symposium on Applied Computing 2005, Information Access and Retrieval Track. March 2005: pdf       (Presentation for SAC-IAR, March 2005)
  26. Exploration Approaches to Adaptive Filtering. Dmitriy Fradkin and Micahel Littman.  DIMACS Technical Report 2005-01: ps.gz       Presentation for a Site Visit
  27. DIMACS at the TREC 2004 Genomics Track. Aynur Dayanik, Dmitriy Fradkin, Alex Genkin, Paul Kantor, David D. Lewis, David Madigan, Vladimir Menkov. Proceedings of the 13th Text REtrieval Conference (TREC 2004): pdf
  28. Distinguishing Mislabeled Data from Correctly Labeled Data in Classifier Design. Sundara Venkataraman, Dimitris Metaxas, Dmitriy Fradkin, Casimir Kulikowski, Ilya Muchnik. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). November 2004: pdf      Presentation for ICTAI'04, Boca Raton, FL November 17, 2004 
  29. A Design Space Approach to Analysis of Information Retrieval Adaptive Filtering Systems. Dmitriy Fradkin and Paul Kantor. Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management (CIKM). November 2004. pdf    Presentation for CIKM'04, Washington, D.C. November 10, 2004 
  30. A Study of K-Means Clustering for Improving Classification Accuracy of Multi-Class SVM. Dmitriy Fradkin and Ilya B. Muchnik.  DIMACS Technical Report 2004-02: ps.gz 
  31. Clusters With Core-Tail Hierarchical Structure And Their Applications To Machine Learning Classification. Dmitriy Fradkin and Ilya B. Muchnik. Proceedings of KIMAS'03 Conference: pdf   Presentation for KIMAS'03, Boston, MA. October 3, 2003 
  32. Image Compression in Real-Time Multiprocessor Systems Using Divisive K-Means Clustering. Dmitriy Fradkin, Ilya B. Muchnik and Simon Streltsov. Proceedings of KIMAS'03 Conferece: pdf 
  33. Experiments with Random Projections for Machine Learning. Dmitriy Fradkin and David Madigan. Proceedings of KDD'03 (edited for space): pdf Longer version: pdf    Presentation at DIMACS Workshop on Discrete Metric Spaces and their Algorithmic Applications, Princeton, NJ, August 20 - 23, 2003; Poster for SIGKDD 
  34. Monotonic Systems and Their Properties.  E. I. Kuznecov, I,B. Muchnik, L.V. Shvartzer. Translated from Russian by D. Fradkin. pdfps

    * Personal use of the material provided here is permitted. Permission from respective authorities must be obtained for all other uses, in any current or future media,including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.