Interval Set Clustering of Web Users with Rough K-Means P Lingras, C West Journal of Intelligent Information Systems 23 (1), 5-16, 2004 | 610 | 2004 |
Decision-theoretic rough set models Y Yao International conference on rough sets and knowledge technology, 1-12, 2007 | 505 | 2007 |
Data mining using extensions of the rough set model PJ Lingras, YY Yao Journal of the American Society for Information Science 49 (5), 415-422, 1998 | 219 | 1998 |
Interpretations of belief functions in the theory of rough sets YY Yao, PJ Lingras Information sciences 104 (1-2), 81-106, 1998 | 198 | 1998 |
Rough neural networks P Lingras Proc. of the 6th Int. Conf. on Information Processing and Management of …, 1996 | 154 | 1996 |
Soft clustering–fuzzy and rough approaches and their extensions and derivatives G Peters, F Crespo, P Lingras, R Weber International Journal of Approximate Reasoning 54 (2), 307-322, 2013 | 153 | 2013 |
Estimation of missing traffic counts using factor, genetic, neural, and regression techniques M Zhong, P Lingras, S Sharma Transportation Research Part C: Emerging Technologies 12 (2), 139-166, 2004 | 143 | 2004 |
Rough set based 1-v-1 and 1-vr approaches to support vector machine multi-classification P Lingras, C Butz Information Sciences 177 (18), 3782-3798, 2007 | 130 | 2007 |
Unsupervised rough set classification using GAs P Lingras Journal of Intelligent Information Systems 16 (3), 215-228, 2001 | 120 | 2001 |
Rough cluster quality index based on decision theory P Lingras, M Chen, D Miao IEEE Transactions on Knowledge and Data Engineering 21 (7), 1014-1026, 2008 | 119 | 2008 |
Comparison of neofuzzy and rough neural networks P Lingras Information Sciences 110 (3-4), 207-215, 1998 | 109 | 1998 |
Rough set clustering for web mining P Lingras 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE …, 2002 | 101 | 2002 |
A decision-theoretic roguth set model, Methodologies for intelligent systems, 5 YY Yao, SKM Wong, P Lingras Elsevier North-Holland, Inc., New York, NY, 1991 | 101 | 1991 |
Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks C Davies, P Lingras European Journal of Operational Research 144 (1), 27-38, 2003 | 99 | 2003 |
Genetically designed models for accurate imputation of missing traffic counts M Zhong, S Sharma, P Lingras Transportation research record 1879 (1), 71-79, 2004 | 81 | 2004 |
Time delay neural networks designed using genetic algorithms for short term inter-city traffic forecasting P Lingras, P Mountford International conference on industrial, engineering and other applications …, 2001 | 81 | 2001 |
Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets P Lingras, M Hogo, M Snorek Web Intelligence and Agent Systems: An International Journal 2 (3), 217-225, 2004 | 80 | 2004 |
Applying rough set concepts to clustering P Lingras, G Peters Rough Sets: Selected Methods and Applications in Management and Engineering …, 2012 | 74 | 2012 |
Granular meta-clustering based on hierarchical, network, and temporal connections P Lingras, F Haider, M Triff Granular Computing 1 (1), 71-92, 2016 | 72 | 2016 |
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach P Lingras, M Hogo, M Snorek, C West Information Sciences 172 (1-2), 215-240, 2005 | 69 | 2005 |