Pawan Lingras

Pawan Lingras, P. Lingras, 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations

Min Chen, Pawan Lingras, Duoqian Miao, 2014, Int. J. Approx. Reason..

Web-based Support Systems (WSS): A Report of the WIC Canada Research Centre

Yiyu Yao, Jingtao Yao, Cory J. Butz, 2004, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04).

A formal comparison of variable elimination and arc reversal in Bayesian network inference

Cory J. Butz, Junying Chen, Pawan Lingras, 2009, Intell. Decis. Technol..

Matching Patterns for Updating Missing Values of Traffic Counts

Ming Zhong, Pawan Lingras, Satish Sharma, 2006 .

Comparison of Conventional and Rough K-Means Clustering

Rui Yan, Pawan Lingras, Chad West, 2003, RSFDGrC.

Soft clustering from crisp clustering using granulation for mobile call mining

Pawan Lingras, Sarjerao Nimse, N. Darkunde, 2011, 2011 IEEE International Conference on Granular Computing.

Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks

Pawan Lingras, Cedric Davies, P. Lingras, 2003, Eur. J. Oper. Res..

UNSUPERVISED LEARNING USING ROUGH KOHONEN NEURAL NETWORK CLASSIFIERS

Pawan Lingras, P. Lingras, 1996 .

AVERAGE AND PEAK TRAFFIC VOLUMES: NEURAL NETS, REGRESSION, FACTOR APPROACHES

Pawan Lingras, Mario Adamo, 1996 .

Proceedings of the 8th International Conference on Rough Sets and Knowledge Technology - Volume 8171

Chris Cornelis, Pawan Lingras, Piotr Wasilewski, 2013 .

Development of a whole building model predictive control strategy for a LEED silver community college

Zheng Qin, Lukas G. Swan, Pawan Lingras, 2016 .

Supermarket Customer Attrition Analysis Based on Cluster Membership Patterns

Pawan Lingras, Chad West, Adish Jain, 2003, IICAI.

Rough possibilistic meta-clustering of retail datasets

Zied Elouedi, Pawan Lingras, Asma Ammar, 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).

Building an Intelligent Web: Theory and Practice

Pawan Lingras, Rajendra A. Akerkar, 2007 .

Analyzing the Performance of Genetically Designed Short-Term Traffic Prediction Models Based on Road Types and Functional Classes

Ming Zhong, Pawan Lingras, Satish Sharma, 2004, IEA/AIE.

Comparing Clustering Schemes at Two Levels of Granularity for Mobile Call Mining

Pawan Lingras, Parag Bhalchandra, Santosh Khamitkar, 2011, RSKT.

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

Fei Xu, Pawan Lingras, Satish Sharma, 2000 .

Analysis of User-Weighted π Rough k-Means

Pawan Lingras, Georg Peters, 2014, RSKT.

The K-Modes Method under Possibilistic Framework

Zied Elouedi, Pawan Lingras, Asma Ammar, 2013, Canadian Conference on AI.

Belief Rough Set Classification for web mining based on dynamic core

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

Towards Implementing Valuation Based Systems with Relational Databases

Yiyu Yao, S. K. Michael Wong, Pawan Lingras, 1991, ISMIS.

CLASSIFYING HIGHWAYS: HIERARCHICAL GROUPING VERSUS KOHONEN NEURAL NETWORKS

Pawan Lingras, P. Lingras, 1995 .

Clustering Ensemble for Prioritized Sampling Based on Average and Rough Patterns

Pawan Lingras, Matt Triff, Ilya Pavlovski, 2017, ISMIS.

Interval Set Representations of Clusters

Pawan Lingras, Rui Yan, Mofreh Hogo, 2005 .

Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models

Pawan Lingras, Ming Zhong, Satish Sharma, 2005 .

Advances in Artificial Intelligence - 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John's, Canada, May 25-27, 2011. Proceedings

Cory J. Butz, Pawan Lingras, 2011, Canadian Conference on AI.

Conventional Versus Interval Clustering Using Kohonen Networks

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2003, ICEIS.

Semantically Segmented Clustering Based on Possibilistic and Rough Set Theories

Zied Elouedi, Pawan Lingras, Asma Ammar, 2015, Int. J. Intell. Syst..

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Ming Zhong, Pawan Lingras, Satish Sharma, 2007 .

Temporal analysis of clusters of supermarket customers: conventional versus interval set approach

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2005, Inf. Sci..

Fuzzy C-Means Clustering of Web Users for Educational Sites

Rui Yan, Pawan Lingras, Chad West, 2003, Canadian Conference on AI.

Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing

Pawan Lingras, P. Lingras, 2001, Neurocomputing.

Rough Support Vectors: Classification, Regression, Clustering

Cory J. Butz, Pawan Lingras, Parag Bhalchandra, 2013, Rough Sets and Intelligent Systems.

Genetically Designed Models for Accurate Imputation of Missing Traffic Counts

Ming Zhong, Pawan Lingras, Satish Sharma, 2004 .

TRAFFIC PARAMETER ESTIMATION AND HIGHWAY CLASSIFICATION: ROUGH PATTERNS USING A NEURAL NETWORKS APPROACH

Pawan Lingras, 1998 .

Interval Set Cluster Analysis: A Re-formulation

Yiyu Yao, Pawan Lingras, Duoqian Miao, 2009, RSFDGrC.

Interpretation of Belief Functions in The Theory of Rough Sets

Yiyu Yao, Pawan Lingras, Yiyu Yao, 1998, Inf. Sci..

Effect of Maximizing Recall and Agglomeration of Feedback on Accuracy

Pawan Lingras, Ross MacDonald, Nikita Neveditsin, 2019, IFSA/NAFIPS.

Rough K-medoids clustering using GAs

Pawan Lingras, P. Lingras, 2009, 2009 8th IEEE International Conference on Cognitive Informatics.

Meta-clustering of possibilistically segmented retail datasets

Zied Elouedi, Pawan Lingras, Asma Ammar, 2016, Fuzzy Sets Syst..

Proceedings of the 24th Canadian conference on Advances in artificial intelligence

Cory J. Butz, Pawan Lingras, 2011 .

Rough support vector regression

Cory J. Butz, Pawan Lingras, P. Lingras, 2010, Eur. J. Oper. Res..

Survey of Rough and Fuzzy Hybridization

Pawan Lingras, Richard Jensen, P. Lingras, 2007, 2007 IEEE International Fuzzy Systems Conference.

The k-modes method using possibility and rough set theories

Zied Elouedi, Pawan Lingras, Asma Ammar, 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

Upper and Lower Entropies of Belief Functions Using Compatible Probability Functions

S. K. Michael Wong, Pawan Lingras, C. W. R. Chau, 1993, ISMIS.

Decremental Rough Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2014, ICAIS.

Hyperlink Structure Inspired by Web Usage

Pawan Lingras, Rucha Lingras, 2010 .

Fuzzy and Crisp Recursive Profiling of Online Reviewers and Businesses

Pawan Lingras, Matt Triff, P. Lingras, 2015, IEEE Transactions on Fuzzy Systems.

On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency

Cory J. Butz, Pawan Lingras, 2005, IICAI.

Temporal Versus Latest Snapshot Web Usage Mining Using Kohonen Som And Modified Kohonen Som Based on The Properties of Rough Sets Theory

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2004, Int. J. Artif. Intell. Tools.

Propagation of knowledge from crisp and soft clustering through a granular hierarchy

Pawan Lingras, Parag Bhalchandra, Santosh Khamitkar, 2012, 2012 12th International Conference on Hybrid Intelligent Systems (HIS).

Cost of Driving Comfort and Convenience for Two‐Lane Highways

Pawan Lingras, Satish Sharma, 1989 .

Analysis of Rough and Fuzzy Clustering

Pawan Lingras, C. Raghavendra Rao, C. R. Rao, 2010, RSKT.

Fuzzy temporal meta-clustering of financial trading volatility patterns

Pawan Lingras, Farhana Haider, Matt Triff, 2017 .

Application of Neural Networks to Estimate AADT on Low-Volume Roads

Fei Xu, Pawan Lingras, Satish Sharma, 2001 .

Genetically-Designed Time Delay Neural Networks for Multiple-interval Urban Freeway Traffic Flow Forecasting

Ming Zhong, Pawan Lingras, Satish Sharma, 2006 .

History of Set Theory and Its Extensions in the Context of Soft Computing

Pawan Lingras, Sarjerao Nimse, 2011, RSKT.

An Algorithm for the Estimation of a Time Period of 2-Sequences

Pawan Lingras, Virendrakumar C. Bhavsar, Manish Joshi, 2009, IICAI.

Evolutionary Regression and Neural Imputations of Missing Values

Ming Zhong, Pawan Lingras, Satish Sharma, 2008, Soft Computing Applications in Industry.

Soft clustering - Fuzzy and rough approaches and their extensions and derivatives

Richard Weber, Pawan Lingras, Fernando A. Crespo, 2013, Int. J. Approx. Reason..

Rough Computing: Theories, Technologies and Applications

Aboul Ella Hassanien, Dominik Slezak, Zbigniew Suraj, 2007 .

Interval Set Clustering of Web Users with Rough K-Means

Pawan Lingras, Chad West, P. Lingras, 2004, Journal of Intelligent Information Systems.

SOME HIGHWAY ECONOMIC ANALYSIS EXPERIENCES WITH THE 1985 HIGHWAY CAPACITY MANUAL. 1986 ANNUAL CONFERENCE PROCEEDINGS VOLUME 2

Pawan Lingras, Satish Sharma, Al Werner, 1986 .

Web Programming and Internet Technologies: An E-Commerce Approach

Pawan Lingras, Porter Scobey, 2012 .

A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference

Cory J. Butz, Junying Chen, Pawan Lingras, 2009, FLAIRS Conference.

Heuristic for Attribute Selection Using Belief Discernibility Matrix

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2012, RSKT.

Evolutionary and Iterative Crisp and Rough Clustering II: Experiments

Pawan Lingras, Manish Joshi, 2009, PReMI.

Propagation of Preference Relations in Qualitative Inference Networks

Yiyu Yao, S. K. Michael Wong, Pawan Lingras, 1991, IJCAI.

ROAD CLASSIFICATION ACCORDING TO DRIVER POPULATION

Pawan Lingras, Satish Sharma, Masood U Hassan, 1986 .

Applications of Rough Genetic Algorithms

Pawan Lingras, Cedric Davies, P. Lingras, 2001, Comput. Intell..

Classification systems based on rough sets under the belief function framework

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2011, Int. J. Approx. Reason..

Statistical, Evolutionary, and Neurocomputing Clustering Techniques: Cluster-Based vs Object-Based Approaches

Pawan Lingras, Xiandong Huang, P. Lingras, 2005, Artificial Intelligence Review.

Rough, fuzzy, interval clustering for web usage mining

Yiyu Yao, Pawan Lingras, Virendrakumar C. Bhavsar, 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

Crisp and Soft Clustering of Mobile Calls

Pawan Lingras, Parag Bhalchandra, Santosh Khamitkar, 2011, MIWAI.

WEB USAGE MINING: COMPARISON OFCONVENTIONAL, FUZZY, AND ROUGH SET CLUSTERING

Rui Yan, Pawan Lingras, Adish Jain, 2004 .

Use of Stability and Seasonality Analysis for Optimal Inventory Prediction Models

Peng Zhang, Pawan Lingras, Manish Joshi, 2011, J. Intell. Syst..

Compatibility of Quantitative and Qualitative Representations of Belief

Yiyu Yao, S. K. Michael Wong, Pawan Lingras, 1994, UAI.

Precision of Rough Set Clustering

Min Chen, Pawan Lingras, Duoqian Miao, 2008, RSCTC.

Rough Cluster Quality Index Based on Decision Theory

Min Chen, Pawan Lingras, Duoqian Miao, 2009, IEEE Transactions on Knowledge and Data Engineering.

Rule Discovery Process Based on Rough Sets under the Belief Function Framework

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2010, IPMU.

Relationship between Product Based Loyalty and Clustering based on Supermarket Visit and Spending Patterns

Pawan Lingras, Greg Adams, Chad West, 2005, Int. J. Comput. Sci. Appl..

Incremental Rough Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2013, MIWAI.

Comparison of Gene Co-expression Networks and Bayesian Networks

Nitesh V. Chawla, Pawan Lingras, Saurabh Nagrecha, 2013, ACIIDS.

Statistical and Genetic Algorithms Classification of Highways

Pawan Lingras, P. Lingras, 2001 .

Neural Networks as Alternative to Traditional Factor Approach of Annual Average Daily Traffic Estimation from Traffic Counts

Fei Xu, Pawan Lingras, Satish Sharma, 1999 .

Combination of Evidence Using the Principle of Minimum Information Gain pdf

S. K. Michael Wong, Pawan Lingras, P. Lingras, 2013, ArXiv.

Representation of Qualitative User Preference by Quantitative Belief Functions

S. K. Michael Wong, Pawan Lingras, 1994, IEEE Trans. Knowl. Data Eng..

Time Complexity of Rough Clustering: GAs versus K-Means

Yiyu Yao, Pawan Lingras, 2002, Rough Sets and Current Trends in Computing.

Scientific Analysis Language for Retrieval and Analysis of Knowledge

Pawan Lingras, 1995 .

Nonlinear classification, linear clustering, evolutionary semi-supervised three-way decisions: A comparison

Pawan Lingras, Matt Triff, Glavin Wiechert, 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Advanced Informatics for Computing Research

Dharm Singh, Balasubramanian Raman, Pawan Lingras, 2017, Communications in Computer and Information Science.

Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers

Cory J. Butz, Pawan Lingras, P. Lingras, 2005, RSFDGrC.

Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering

Pawan Lingras, Matt Triff, P. Lingras, 2016, IJCRS.

Precision and Recall in Rough Support Vector Machines

Cory J. Butz, Pawan Lingras, 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

Partially ordered rough ensemble clustering for multigranular representations

Pawan Lingras, Farhana Haider, P. Lingras, 2015, Intell. Data Anal..

Supervised and Semi-Supervised Identification of Users and Activities from Wearable Device Brain Signal Recordings

Pawan Lingras, Matt Triff, Shuai Zhao, 2017 .

Belief Rough Set Classifier

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2009, Canadian Conference on AI.

Granular meta-clustering based on hierarchical, network, and temporal connections

Pawan Lingras, Farhana Haider, Matt Triff, 2016 .

Interval set representations of 1-v-r support vector machine multi-classifiers

Cory J. Butz, Pawan Lingras, P. Lingras, 2005, 2005 IEEE International Conference on Granular Computing.

Semi-supervised Rough Cost/Benefit Decisions

Min Chen, Pawan Lingras, Duoqian Miao, 2009, Fundam. Informaticae.

Computational Intelligence in Decision Making

Yuefeng Li, Tianrui Li, Joseph P. Herbert, 2011, Int. J. Comput. Intell. Syst..

Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification

Cory J. Butz, Pawan Lingras, P. Lingras, 2007, Inf. Sci..

Semantically Enhanced Clustering in Retail Using Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2014, RSKT.

Rough Set Year in India 2009

Dominik Slezak, Pawan Lingras, Nitin Patil, 2009, RSFDGrC.

Multi-disciplinary Trends in Artificial Intelligence: 7th International Workshop, MIWAI 2013, Krabi, Thailand, December 9-11, 2013)

Aneesh Krishna, Pawan Lingras, Chattrakul Sombattheera, 2013 .

Iterative meta-clustering through granular hierarchy of supermarket customers and products

Zied Elouedi, Pawan Lingras, Asma Ammar, 2014, Inf. Sci..

Classification with Dynamic Reducts and Belief Functions

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2011, Trans. Rough Sets.

Rough Sets and Knowledge Technology: 8th International Conference, RSKT 2013, Halifax, NS, Canada, October 11-14, 2013, Proceedings

Chris Cornelis, Pawan Lingras, Piotr Wasilewski, 2013 .

Clustering of Web Users: K-Means vs. Fuzzy C-Means

Rui Yan, Pawan Lingras, Adish Jain, 2003, Indian International Conference on Artificial Intelligence.

Web-based Support Systems (WSS): A Report of the WIC Canada Research Centre

Yiyu Yao, Jingtao Yao, Cory J. Butz, 2004, IEEE/WIC/ACM International Conference on Web Intelligence (WI'04).

Evolutionary semi-supervised rough categorization of brain signals from a wearable headband

Pawan Lingras, Matt Triff, Shuai Zhao, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

Book Review: Big Data Analytics

Sugata Sanyal, Pawan Lingras, 2015, IEEE Intell. Informatics Bull..

Time Delay Neural Networks Designed Using Genetic Algorithms for Short Term Inter-City Traffic Forecasting

Pawan Lingras, Paul Mountford, P. Lingras, 2001, IEA/AIE.

Evidential Comparisons Using Belief Functions, Rough Sets and Nonmonotonic Preferences

Pawan Lingras, 1996, Intell. Autom. Soft Comput..

A Comparison of Dynamic and Static Belief Rough Set Classifier

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2010, RSCTC.

Applications of Rough Set Based K-Means, Kohonen SOM, GA Clustering

Pawan Lingras, P. Lingras, 2007, Trans. Rough Sets.

Recursive meta-clustering in a granular network

Pawan Lingras, Kishore Rathinavel, P. Lingras, 2012, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).

Refining Genetically Designed Models for Improved Traffic Prediction on Rural Roads

Ming Zhong, Pawan Lingras, Satish Sharma, 2005 .

Unsupervised Characterization of Web Users: Conventional and Fuzzy Approaches

Rui Yan, Pawan Lingras, Adish Jain, 2004 .

A decision-theoretic roguth set model

Yiyu Yao, Pawan Lingras, S. K. M. Wong, 1991 .

Join tree propagation utilizing both arc reversal and variable elimination

Cory J. Butz, Pawan Lingras, Ken Konkel, 2009, Int. J. Approx. Reason..

Data Mining Using Extensions of the Rough Set Model

Yiyu Yao, Pawan Lingras, Yiyu Yao, 1998, J. Am. Soc. Inf. Sci..

Temporal Web usage mining

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

Rough Set Based Clustering: Evolutionary, Neural, and Statistical Approaches

Rui Yan, Pawan Lingras, Mofreh Hogo, 2003, IICAI.

Visual Predictions of Traffic Conditions

Pawan Lingras, Jason P. Rhinelander, Mathew Kallada, 2015, Canadian Conference on AI.

Applying Rough Set Concepts to Clustering

Pawan Lingras, Georg Peters, P. Lingras, 2012 .

Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011, Tumkur, Karnataka State, India, December 14-16, 2011

Ramakant Nevatia, Pawan Lingras, Bhanu Prasad, 2011, IICAI.

Book Review: Cognitive Computing: Theory and Applications

Pawan Lingras, 2016, IEEE Intell. Informatics Bull..

Prediction of Recreational Travel Using Genetically Designed Regression and Time-Delay Neural Network Models

Ming Zhong, Pawan Lingras, Satish Sharma, 2002 .

Experimental Comparison of Iterative Versus Evolutionary Crisp and Rough Clustering

Pawan Lingras, Manish Joshi, P. Lingras, 2011, Int. J. Comput. Intell. Syst..

Clustering of Products to Identify Optimal Inventory Prediction Models

Peng Zhang, Pawan Lingras, Manish Joshi, 2011, IICAI.

Evidential Clustering or Rough Clustering: The Choice Is Yours

Pawan Lingras, Manish Joshi, 2012, RSKT.

Decremental Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2013, SCAI.

Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings

Runhe Huang, Pawan Lingras, Sheila Petty, 2010, AMT.

Clustering Supermarket Customers Using Rough Set Based Kohonen Networks

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2003, ISMIS.

Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets

Pawan Lingras, Mofreh Hogo, Miroslav Snorek, 2004, Web Intell. Agent Syst..

Rough clustering

Pawan Lingras, Georg Peters, P. Lingras, 2011, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

RPKM: The Rough Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2012, ISMIS.

Proceedings of the 7th International Workshop on Multi-disciplinary Trends in Artificial Intelligence - Volume 8271

Aneesh Krishna, Pawan Lingras, Sheela Ramanna, 2013 .

Incremental Possibilistic K-Modes

Zied Elouedi, Pawan Lingras, Asma Ammar, 2013, RSFDGrC.

Recursive Profiles of Businesses and Reviewers on Yelp.com

Pawan Lingras, Matt Triff, 2013, RSFDGrC.

Combination of evidence in rough set theory

Pawan Lingras, 1993, Proceedings of ICCI'93: 5th International Conference on Computing and Information.

Rough Sets and Knowledge Technology

Chris Cornelis, Pawan Lingras, Piotr Wasilewski, 2013, Lecture Notes in Computer Science.

Rough Multi-category Decision Theoretic Framework

Min Chen, Pawan Lingras, Duoqian Miao, 2008, RSKT.

A granular recursive fuzzy meta-clustering algorithm for social networks

Pawan Lingras, Kishore Rathinavel, P. Lingras, 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

WI 2012 Welcome Message from Conference Chairs and Program Chairs

Pawan Lingras, Ning Zhong, Piotr S. Szczepaniak, 2012, WI 2012.

Using Speech Recognition and Intelligent Search Tools to Enhance Information Accessibility

Pawan Lingras, Keith Bain, Jason Hines, 2007, HCI.

Dynamic Reduct from Partially Uncertain Data Using Rough Sets

Zied Elouedi, Pawan Lingras, Salsabil Trabelsi, 2009, RSFDGrC.

ESTIMATION OF AADT VOLUMES USING NEURAL NETWORKS

Pawan Lingras, M Adamo, 1995 .

Web Document Clustering Using Fuzzy Equivalence Relations

George Karypis, Pawan Lingras, Michael Steinbach, 2011 .

Automatic Determination of Learning Styles

Pawan Lingras, Manish Joshi, Ravindra Vaidya, .

A three-way cluster ensemble approach for large-scale data

Guoyin Wang, Pawan Lingras, Yun Chen, 2019, Int. J. Approx. Reason..

COMPARATIVE BELIEFS AND THEIR MEASUREMENTS

Yiyu Yao, Pawan Lingras, Yiyu Yao, 1993 .