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Pawan Lingras: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. S. K. Michael Wong, Pawan Lingras
    Representation of Qualitative User Preference by Quantitative Belief Functions. [Citation Graph (1, 2)][DBLP]
    IEEE Trans. Knowl. Data Eng., 1994, v:6, n:1, pp:72-78 [Journal]
  2. Pawan Lingras, Rui Yan, Chad West
    Fuzzy C-Means Clustering of Web Users for Educational Sites. [Citation Graph (0, 0)][DBLP]
    Canadian Conference on AI, 2003, pp:557-562 [Conf]
  3. Pawan Lingras
    Combination of Evidence in Rough Set Theory. [Citation Graph (0, 0)][DBLP]
    ICCI, 1993, pp:289-293 [Conf]
  4. Mofreh Hogo, Pawan Lingras, Miroslav Snorek
    Conventional Versus Interval Clustering Using Kohonen Networks. [Citation Graph (0, 0)][DBLP]
    ICEIS (2), 2003, pp:250-257 [Conf]
  5. Pawan Lingras, Paul Mountford
    Time Delay Neural Networks Designed Using Genetic Algorithms for Short Term Inter-City Traffic Forecasting. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2001, pp:290-299 [Conf]
  6. Ming Zhong, Satish Sharma, Pawan Lingras
    Analyzing the Performance of Genetically Designed Short-Term Traffic Prediction Models Based on Road Types and Functional Classes. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2004, pp:1133-1145 [Conf]
  7. Cory J. Butz, Pawan Lingras
    On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency. [Citation Graph (0, 0)][DBLP]
    IICAI, 2005, pp:2464-2475 [Conf]
  8. Pawan Lingras, Rui Yan, Mofreh Hogo
    Rough Set Based Clustering: Evolutionary, Neural, and Statistical Approaches. [Citation Graph (0, 0)][DBLP]
    IICAI, 2003, pp:1074-1087 [Conf]
  9. Pawan Lingras, Rui Yan, Adish Jain
    Clustering of Web Users: K-Means vs. Fuzzy C-Means. [Citation Graph (0, 0)][DBLP]
    IICAI, 2003, pp:1062-1073 [Conf]
  10. Chad West, Adish Jain, Pawan Lingras, Bill Leonard
    Supermarket Customer Attrition Analysis Based on Cluster Membership Patterns. [Citation Graph (0, 0)][DBLP]
    IICAI, 2003, pp:1132-1140 [Conf]
  11. S. K. Michael Wong, Pawan Lingras, Yiyu Yao
    Propagation of Preference Relations in Qualitative Inference Networks. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1991, pp:1204-1209 [Conf]
  12. C. W. R. Chau, Pawan Lingras, S. K. Michael Wong
    Upper and Lower Entropies of Belief Functions Using Compatible Probability Functions. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1993, pp:306-315 [Conf]
  13. Pawan Lingras, Mofreh Hogo, Miroslav Snorek, Bill Leonard
    Clustering Supermarket Customers Using Rough Set Based Kohonen Networks. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2003, pp:169-173 [Conf]
  14. Pawan Lingras, S. K. Michael Wong
    An Optimistic Rule for Accumulation of Evidence. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1988, pp:60-69 [Conf]
  15. S. K. Michael Wong, Pawan Lingras, Yiyu Yao
    Towards Implementing Valuation Based Systems with Relational Databases. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1991, pp:172-182 [Conf]
  16. Pawan Lingras, Y. Y. Yao
    Time Complexity of Rough Clustering: GAs versus K-Means. [Citation Graph (0, 0)][DBLP]
    Rough Sets and Current Trends in Computing, 2002, pp:263-270 [Conf]
  17. Pawan Lingras, Cory J. Butz
    Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers. [Citation Graph (0, 0)][DBLP]
    RSFDGrC (2), 2005, pp:166-173 [Conf]
  18. Pawan Lingras, Cedric Davies
    Rough Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    RSFDGrC, 1999, pp:38-46 [Conf]
  19. Pawan Lingras, Rui Yan, Chad West
    Comparison of Conventional and Rough K-Means Clustering. [Citation Graph (0, 0)][DBLP]
    RSFDGrC, 2003, pp:130-137 [Conf]
  20. S. K. Michael Wong, Yiyu Yao, Pawan Lingras
    Compatibility of Quantitative and Qualitative Representations of Belief. [Citation Graph (0, 0)][DBLP]
    UAI, 1991, pp:418-424 [Conf]
  21. Mofreh Hogo, Miroslav Snorek, Pawan Lingras
    Temporal Web Usage Mining. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2003, pp:450-453 [Conf]
  22. Pawan Lingras, Mofreh Hogo, Miroslav Snorek
    Temporal Cluster Migration Matrices for Web Usage Mining. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2004, pp:441-444 [Conf]
  23. Yiyu Yao, JingTao Yao, Cory J. Butz, Pawan Lingras, Dawn N. Jutla
    Web-based Support Systems (WSS): A Report of the WIC Canada Research Centre. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2004, pp:787-788 [Conf]
  24. Pawan Lingras, Cory J. Butz
    Interval set representations of 1-v-r support vector machine multi-classifiers. [Citation Graph (0, 0)][DBLP]
    GrC, 2005, pp:193-198 [Conf]
  25. Pawan Lingras, Xiandong Huang
    Statistical, Evolutionary, and Neurocomputing Clustering Techniques: Cluster-Based vs Object-Based Approaches. [Citation Graph (0, 0)][DBLP]
    Artif. Intell. Rev., 2005, v:23, n:1, pp:3-29 [Journal]
  26. Pawan Lingras, Cedric Davies
    Application of Rough Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    Computational Intelligence, 2001, v:17, n:3, pp:435-445 [Journal]
  27. Mofreh Hogo, Miroslav Snorek, Pawan Lingras
    Temporal Versus Latest Snapshot Web Usage Mining Using Kohonen Som And Modified Kohonen Som Based on The Properties of Rough Sets Theory. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2004, v:13, n:3, pp:569-592 [Journal]
  28. Pawan Lingras, S. K. Michael Wong
    Two Perspectives of the Dempster-Shafer Theory of Belief Functions. [Citation Graph (0, 0)][DBLP]
    International Journal of Man-Machine Studies, 1990, v:33, n:4, pp:467-487 [Journal]
  29. Pawan Lingras
    Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2001, v:36, n:1-4, pp:29-44 [Journal]
  30. Pawan Lingras
    Comparison of Neofuzzy and Rough Neural Networks. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1998, v:110, n:3-4, pp:207-215 [Journal]
  31. Pawan Lingras, Mofreh Hogo, Miroslav Snorek, Chad West
    Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2005, v:172, n:1-2, pp:215-240 [Journal]
  32. Y. Y. Yao, Pawan Lingras
    Interpretation of Belief Functions in The Theory of Rough Sets. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1998, v:104, n:1-2, pp:81-106 [Journal]
  33. Pawan Lingras, Y. Y. Yao
    Data Mining Using Extensions of the Rough Set Model. [Citation Graph (0, 0)][DBLP]
    JASIS, 1998, v:49, n:5, pp:415-422 [Journal]
  34. Pawan Lingras
    Unsupervised Rough Set Classification Using GAs. [Citation Graph (0, 0)][DBLP]
    J. Intell. Inf. Syst., 2001, v:16, n:3, pp:215-228 [Journal]
  35. Pawan Lingras, Chad West
    Interval Set Clustering of Web Users with Rough K-Means. [Citation Graph (0, 0)][DBLP]
    J. Intell. Inf. Syst., 2004, v:23, n:1, pp:5-16 [Journal]
  36. Pawan Lingras, Mofreh Hogo, Miroslav Snorek
    Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets. [Citation Graph (0, 0)][DBLP]
    Web Intelligence and Agent Systems, 2004, v:2, n:3, pp:217-225 [Journal]
  37. Cedric Davies, Pawan Lingras
    Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks. [Citation Graph (0, 0)][DBLP]
    European Journal of Operational Research, 2003, v:144, n:1, pp:27-38 [Journal]
  38. Keith Bain, Jason Hines, Pawan Lingras, Yumei Qin
    Using Speech Recognition and Intelligent Search Tools to Enhance Information Accessibility. [Citation Graph (0, 0)][DBLP]
    HCI (7), 2007, pp:214-223 [Conf]
  39. Pawan Lingras, Cory J. Butz
    Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2007, v:177, n:18, pp:3782-3798 [Journal]
  40. Pawan Lingras
    Applications of Rough Set Based K-Means, Kohonen SOM, GA Clustering. [Citation Graph (0, 0)][DBLP]
    T. Rough Sets, 2007, v:7, n:, pp:120-139 [Journal]
  41. Chad West, Stephanie MacDonald, Pawan Lingras, Greg Adams
    Relationship between Product Based Loyalty and Clustering based on Supermarket Visit and Spending Patterns. [Citation Graph (0, 0)][DBLP]
    IJCSA, 2005, v:2, n:2, pp:85-100 [Journal]

  42. Rough K-medoids clustering using GAs. [Citation Graph (, )][DBLP]


  43. Belief Rough Set Classifier. [Citation Graph (, )][DBLP]


  44. A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference. [Citation Graph (, )][DBLP]


  45. Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination. [Citation Graph (, )][DBLP]


  46. Survey of Rough and Fuzzy Hybridization. [Citation Graph (, )][DBLP]


  47. Adaptive hyperlinks Using Page Access Sequences and Minimum Spanning Trees. [Citation Graph (, )][DBLP]


  48. An Algorithm for the Estimation of a Time Period of 2-Sequences. [Citation Graph (, )][DBLP]


  49. Rule Discovery Process Based on Rough Sets under the Belief Function Framework. [Citation Graph (, )][DBLP]


  50. A Web-Based Interface for Hiding Bayesian Network Inference. [Citation Graph (, )][DBLP]


  51. Evolutionary and Iterative Crisp and Rough Clustering I: Theory. [Citation Graph (, )][DBLP]


  52. Evolutionary and Iterative Crisp and Rough Clustering II: Experiments. [Citation Graph (, )][DBLP]


  53. Precision of Rough Set Clustering. [Citation Graph (, )][DBLP]


  54. A Comparison of Dynamic and Static Belief Rough Set Classifier. [Citation Graph (, )][DBLP]


  55. Dynamic Reduct from Partially Uncertain Data Using Rough Sets. [Citation Graph (, )][DBLP]


  56. Rough Set Year in India 2009. [Citation Graph (, )][DBLP]


  57. Interval Set Cluster Analysis: A Re-formulation. [Citation Graph (, )][DBLP]


  58. Rough Multi-category Decision Theoretic Framework. [Citation Graph (, )][DBLP]


  59. Evolutionary Rough K-Means Clustering. [Citation Graph (, )][DBLP]


  60. Precision and Recall in Rough Support Vector Machines. [Citation Graph (, )][DBLP]


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