News:
2007 NSF Next Generation Data Mining Symposium
Thought-Highlights:
Economics of privacy-preserving data mining (ICDE'07 panel)
Data Mining in Vehicular Sensor Networks: Technical and Marketing Challenges (KDD'07 talk)
Thoughts on Human Emotions, Communication Breakthroughs, and the Next Generation of Data Mining (NGDM'07 talk)

Books

 


Data Mining: Next Generation Challenges and Future Directions
H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha
MIT/AAAI Press (to be released in early 2004)

Advances in Distributed and Parallel Knowledge Discovery
Edited by Hillol Kargupta and Philip Chan

 

Distributed and Ubiquitous Data Mining:

Distributed Data Mining, Peer-to-Peer Data Mining, and Ensembles

Data Stream Mining

Privacy Preserving Data Mining

 

Distributed Data Mining, Peer-to-Peer Data Mining, and Ensembles

  1. C. Giannella, K. Liu, H. Kargupta: Breaching Euclidean distance-preserving data perturbation using few known inputs. Data Knowl. Eng. 83: 93-110 (2013).
  2. J. W. Branch, C. Giannella, B. K. Szymanski, R. Wolff, H. Kargupta: In-network outlier detection in wireless sensor networks. Knowl. Inf. Syst. 34(1): 23-54 (2013).
  3. K. Morik, K. Bhaduri, H. Kargupta: Introduction to data mining for sustainability. Data Min. Knowl. Discov. 24(2): 311-324 (2012).
  4. X. Zhu, T. Mahule, H. Dutta, S. Arora, H. Kargupta, K. D. Borne: Peer-to-peer distributed text classifier learning in PADMINI. Statistical Analysis and Data Mining 5(5): 446-462 (2012).
  5. H. Kargupta: Connected Cars: How Distributed Data Mining Is Changing the Next Generation of Vehicle Telematics Products. S-CUBE 2012: 73-74 (2012).
  6. K. Das, K. Bhaduri, H. Kargupta: Multi-objective optimization based privacy preserving distributed data mining in Peer-to-Peer networks. Peer-to-Peer Networking and Applications 4(2): 192-209 (2011).
  7. H. Kargupta. (2011) Making Data Analysis Ubiquitous: My Journey through the Academia and the Industry. The Journeys of Great Data Mining Scientist. Editor: M. Gaber. Springer.
  8. H. Kargupta. (2011) Making Data Analysis Ubiquitous: My Journey through the Academia and the Industry. The Journeys of Great Data Mining Scientist. Editor: M. Gaber. Springer.
  9. R. Mallik, N. Sarda, and H. Kargupta. (2011). Distributed Data Mining for Sustainable Smart Grids. ACM Proceedings of the Sustainable KDD Workshop. KDD 2011, San Diego.
  10. R. Mallik and H. Kargupta. (2011). A Sustainable Approach for Demand Prediction in Smart Grids using a Distributed Local Asynchronous Algorithm. Accepted for publication in the Proceedings of the Conference on Data Understanding (CIDU).
  11. K. Bhaduri, K. Das, K. Borne, C. Giannella, T. Mahule, H. Kargupta. (2011) Scalable, Asynchronous, Distributed Eigen-Monitoring of Astronomy Data Streams. Statistical Analysis and Data Mining Journal. Volume 4, Issue 3, pp. 336-352. June 2011. pdf
  12. K. Das, K. Bhaduri, S. Arora, W. Griffin, K. Borne, C. Giannella, H. Kargupta. (2009). Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms. SIAM International Conference on Data Mining, Nevada. 2009. Abstract
  13. S. Datta and H. Kargupta.(2008). A Communication Efficient Probabilistic Algorithm for Mining Frequent Itemset from Peer-to-Peer Network. Statistical Analysis and Data Mining Journal. (In communication). Abstract
  14. S. Datta, C. Giannella and H. Kargupta. (2008). Approximate K-means Clustering Over a Peer-to-peer Network. IEEE Transactions on Knowledge and Data Engineering. (In communication). Abstract
  15. R. Wolff, K. Bhaduri, H. Kargupta. (2008). A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering. Volume 21, Issue 4, pp. 465-478. April 2009. Abstract pdf
  16. K. Bhaduri, H. Kargupta. (2008). A Scalable Local Algorithm for Distributed Multivariate Regression. Statistical Analysis and Data Mining Journal (Accepted, in press). Abstract pdf
  17. K. Bhaduri, R. Wolff, C. Giannella, H. Kargupta. (2008). Distributed Decision Tree Induction in Peer-to-Peer Systems. Statistical Analysis and Data Mining. Volume 1, Issue 2, pp. 85-103. Abstract pdf
  18. K. Bhaduri, H. Kargupta. (2008). An Efficient Local Algorithm for Distributed Multivariate Regression in Peer-to-Peer Networks. SIAM International Conference on Data Mining, Atlanta, Georgia. pp. 153-164. (Best of SDM'08). Abstract pdf
  19. K. Das, K Bhaduri, K. Liu, and H. Kargupta. Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network. IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 20, No. 4, pp. 475-488, April 2008. Abstract pdf
  20. H. Kargupta. (2007). Thoughts on Human Emotions, Communication Breakthroughs, and the Next Generation of Data Mining. National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation. Abstract pdf
  21. S. Datta, H. Kargupta. (2007). Uniform Data Sampling from a Peer-to-Peer Network. Proceedings of the 2007 IEEE International Conference on Distributed Computing Systems (ICDCS 2007), Toronto, Canada, June, 2007, pp. 50.   Abstract pdf
  22. H. Dutta, C. Giannella, K. Borne and H. Kargupta. (2007). Distributed Top-K Outlier Detection from Astronomy Catalogs using the DEMAC System. Proceedings of the SIAM International Conference on Data Mining, Minneapolis, USA, April 2007.   Abstract pdf
  23. S. Mukherjee, H. Kargupta. (2007). Distributed Probabilistic Inferencing in Sensor Networks using Variational Approximation. Journal of Parallel and Distributed Computing (JPDC), Volume 68, Issue 1, January 2008, Pages 78-92.   Abstract pdf
  24. S. Datta, K. Bhaduri, C. Giannella, R. Wolff, H. Kargupta. (2006). Distributed Data Mining in Peer-to-Peer Networks. (Invited submission to the IEEE Internet Computing special issue on Distributed Data Mining), Volume 10, Number 4, pp. 18--26.  Abstract pdf
  25. J. Branch, B. Szymanski, R. Wolff, C. Gianella, H. Kargupta. (2006). In-Network Outlier Detection in Wireless Sensor Networks. Proceedings of the 26th International Conference on Distributed Computing Systems (ICDCS), 2006. pdf
  26. R. Wolff, K. Bhaduri, H. Kargupta. (2006). Local L2 Thresholding Based Data Mining in Peer-to-Peer Systems. Proceedings of the SIAM International Conference in Data Mining (SDM 06), Bethesda, Maryland, USA, 2006, pp. 430-441. Abstract pdf
  27. S. Datta, C. Giannella, H. Kargupta. (2006). K-Means Clustering over a Large, Dynamic Network. Proc. of the SIAM International Conference in Data Mining (SDM 06), Bethesda, Maryland, USA, 2006, pp. 153-164. Abstract pdf
  28. C. Giannella, H. Dutta, K. Borne, R. Wolff, H. Kargupta. (2006). Distributed Data Mining in Astronomy Catalogs. Special Issue of Concurrency and Computation: Practice and Experience, 2006 (In Communication). Abstract pdf
  29. C. Giannella, H. Dutta, K. Borne, R. Wolff, H. Kargupta. (2006). Distributed Data Mining for Astronomy Catalogs. Proceedings of 9th Workshop on Mining Scientific and Engineering Datasets, as part of the SIAM International Conference on Data Mining (SDM), 2006. Abstract pdf
  30. C. Giannella, H. Dutta, S. Mukherjee, H. Kargupta. (2006). Efficient Kernel Density Estimation Over Distributed Data.Proceedings of the 9th International Workshop on High Performance and Distributed Mining, SIAM International Data Mining Conference, Bethesda, USA . April, 2006. Abstract pdf
  31. K. Liu, K. Bhaduri, K. Das, P. Nguyen, and H. Kargupta. Client-side web mining for community formation in peer-to-peer environments. SIGKDD Explorations, 8(2): 11-20, December 2006. (This paper was selected as the most interesting paper of WedKDD'06.)Abstract pdf
  32. R. Wolff, K. Bhaduri, H. Kargupta. (2006)Monitoring Any Data Model in a Large Distributed System.   In communication. 2006. Abstract
  33. H. Kargupta, B. Park, H. Dutta. (2006). Orthogonal Decision Trees. IEEE Transactions on Knowledge and Data Engineering, volume 18, number 7, pp. 1028-1042. Abstract pdf
     
  34. J. da Silva, C. Giannella, R. Bhargava, H. Kargupta, and M. Klusch. (2005) Distributed Data Mining and Agents, Invited submission, Engineering Applications of Artificial Intelligence Journal. volume 18, pp. 791--807.
  35. H. Kargupta and M. Klein. (2005). Mining Vehicle Data Streams and Privacy-Preserving Driving Characterization. Proceedings of the 84th Annual Transportation Research Board (TRB) meeting. Washington, DC.
  36. H. Kargupta and H. Dutta (2004). Orthogonal Decision Trees. The Fourth IEEE International Conference on Data Mining. Brighton , UK , pp. 487—490. Abstract pdf
  37. D. Meng, K. Sivakumar, and H. Kargupta. (2004). Privacy Sensitive Bayesian Network Parameter Learning. Proceedings of the Fourth IEEE International Conference on Data Mining. Brighton, UK, pp. 427—430.
  38. Kargupta, H. and Park, B. (2004). A Fourier Spectrum-Based Approach to Represent Decision Trees for Mining Data Streams in Mobile Environments. IEEE Transaction on Knowledge and Data Engineering, Volume 16, Number 2, pp. 216--229. pdf
  39. H. Kargupta and K. Sivakumar, (2004) Existential Pleasures of Distributed Data Mining. Data Mining: Next Generation Challenges and Future Directions. Editors: H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha. AAAI/MIT Press. (A review paper on Distributed Data Mining)
  40. K. Sivakumar, R. Chen, and H. Kargupta. (2003). Learning Bayesian Network Structure from Distributed Data. Proceedings of the SIAM International Conference in Data Mining, San Franciso, USA, pp. 284-288.
  41. C. Giannella, K. Liu, T. Olsen, and H. Kargupta. (2004). Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data. Proceedings of the Fourth IEEE International Conference on Data Mining. Brighton, UK, 2004, pp. 67--74. Abstract pdf
  42. R. Chen, K. Sivakumar, and H. Kargupta. (2004). Collective Mining of Bayesian Networks from Heterogeneous Data. Knowledge and Information Systems Journal, volume 6, number 2, pp. 164—187.. (A paper on distributed Bayesian Network learning from heterogeneous data) pdf
  43. H. Kargupta, K. Liu, and J. Ryan. (2003). Privacy Sensitive Distributed Data Mining from Multi-Party Data. Proceedings of the NSF/NIJ Symposium on Intelligence and Security Informatics, pp. 336—342.
  44. R. Bhargava, H. Kargupta, and M. Powers. (2003). Energy Consumption in Data Analysis for On-board and Distributed Applications. Proceedings of the ICML'03 workshop on Machine Learning Technologies for Autonomous Space Applications. (An experimental study of the energy consumption characteristics of some popular data analysis techniques.) pdf
  45. S. Pittie, H. Kargupta, and B. Park. (2003). Dependency Detection in MobiMine: A Systems Perspective. Information Sciences Journal. Volume 155, Issues 3-4, pp. 227-243, Elsevier.
  46. V. Hingne, A. Joshi, T. Finin, H. Kargupta, E. Houstis. (2003). Towards a Pervasive Grid. International Parallel and Distributed Processing Symposium (IPDPS'03). pp. 207.
  47. K. Sivakumar, R. Chen, and H. Kargupta. (2003). Learning Bayesian Network Structure from Distributed Data. Proceedings of the SIAM International Data Mining Conference. San Franciso, USA, pp. 284-288.
  48. B. Park and H. Kargupta (2002). Distributed Data Mining: Algorithms, Systems, and Applications. Data Mining Handbook. Editor: Nong Ye. (A review paper on Distributed Data Mining) pdf
  49. B. Park and H. Kargupta (2002). Constructing Simpler Decision Trees from Ensemble Models Using Fourier Analysis. Proceedings of the 7th Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM SIGMOD 2002. Pp. 18--23.
  50. D. Hershberger and H. Kargupta. (2001).  Distributed Multivariate Regression Using Wavelet-Based Collective Data Mining. Journal of Parallel and Distributed Computing, Volume 61, Number 3, pp. 372--400. pdf
  51. R. Chen, S. Krishnamoorthy, and H. Kargupta. (2001). Distributed Web Mining using Bayesian Networks from Multiple Data Streams. Proceedings of the IEEE International Conference on Data Mining, November 2001, pp. 281--288. IEEE Press.
  52. H. Kargupta, W. Huang, S. Krishnamurthy, B. Park, and S. Wang. (2000). Collective PCA from Distributed and Heterogeneous Data. Proceedings of The Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2000, pp. 452--457, Springer Verlag.
  53. H. Kargupta , Huang, W., Krishnamoorthy,, and Johnson, E. (2001). Distributed Clustering Using Collective Principal Component Analysis. Knowledge and Information Systems Journal. Volume 3, Number 4, pp. 422--448. (A paper on distributed clustering and principal component analysis from heterogeneous data). ps.gz
  54. B. Park, H. Kargupta, E. Johnson, E. Sanseverino, D. Hershberger, and L. Silvestre. (2001). Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique. Journal of Applied Intelligence, volume 16, number 1, pp. 19-42.
  55. E. Johnson and H. Kargupta. (1999). Collective, Hierarchical Clustering from Distributed, Heterogeneous Data. Large-Scale Parallel KDD Systems, Lecture Notes in Computer Science, Eds: Zaki, M. and Ho, C. Vol. 1759, pp. 221-244, Springer Verlag.
  56. H. Kargupta, B. Park, D. Hershberger, and E. Johnson (1999). Collective Data Mining: A New Perspective Toward Distributed Data Mining. Advances in Distributed and Parallel Knowledge Discovery, Eds: Hillol Kargupta and Philip Chan. MIT/AAAI Press. pdf
  57. H. Kargupta, I. Hamzaoglu and B. Stafford. (1997). Scalable, Distributed Data Mining Using an Agent-Based Architecture. Proceedings of Knowledge Discovery and Data Mining. Eds: D. Heckerman, H. Mannila, D. Pregibon and R. Uthurusamy, pp. 211—214, AAAI Press. pdf
  58. H. Kargupta, I. Hamzaoglu, B. Stafford , V. Hanagandi, & K. Buescher (1996). PADMA: Parallel Data Mining Agents For Scalable Text Classification. Proceedings Of The High Performance Computing'97.

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Data Stream Mining

  1. H. Kargupta, V. Puttagunta, M. Klein, K. Sarkar (2006). On-board Vehicle Data Stream Monitoring using MineFleet and Fast Resource Constrained Monitoring of Correlation Matrices. Next Generation Computing. Invited submission for special issue on learning from data streams, volume 25, no. 1, pp. 5--32, 2007. Abstract pdf
  1. S. Bandyopadhyay, C. Gianella, U. Maulik, H. Kargupta, K. Liu, and S. Datta. (2006). Clustering Distributed Data Streams in Peer-to-Peer Environments. Information Sciences , 176(14), 1952-1985,2006. Abstract pdf

2.      H. Dutta, H. Kargupta, and A. Joshi. (2005). Orthogonal Decision Trees for Resource-Constrained Physiological Data Stream Monitoring using Mobile Devices. Proceedings of the High Performance Computing Conference. (In press). Abstract pdf

3.      H. Kargupta, R. Bhargava, K. Liu, M. Powers, P. Blair, S. Bushra, J. Dull, K. Sarkar, M. Klein, M. Vasa, and D. Handy. (2004). VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. Proceedings of the SIAM International Data Mining Conference, Orlando . Abstract pdf

  1. H. Kargupta, K. Sivakumar, S. Ghosh (2002). Random Matrix-Based Approach for Dependency Detection from Data Streams. Proceedings of the 7th Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM SIGMOD 2002. Pp. 18--23.
  2. H. Kargupta, K. Sivakumar, and S. Ghosh. (2002). Dependency Detection in MobiMine and Random Matrices. Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pp. 250--262. Helsinki, Finland . pdf

  3. H. Kargupta, B. Park, S. Pittie, L. Liu, D. Kushraj, and K. Sarkar (2002). MobiMine: Monitoring the Stock Market from a PDA. ACM SIGKDD Explorations. January 2002. Volume 3, Issue 2. Pp. 37--46. ACM Press. ps.gz pdf

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Privacy-Preserving Distributed Data Mining

1. K. Das, K. Bhaduri, H. Kargupta. Multi-objective Optimization Based Privacy Preserving Distributed Data Mining in Peer-to-peer Networks. Peer-to-Peer Networking and Applications. Volume 4, Issue 2, pp. 192-209. 2011. pdf

2. K. Das, K. Bhaduri, H. Kargupta. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks. Knowledge and Information Systems Journal. Volume 24, Issue 3, pp. 341-367. September 2010. pdf     

3.  K. Das, K. Bhaduri, H. Kargupta. A Local Distributed Peer-to-Peer Algorithm Using Multi-Party Optimization Based Privacy Preservation for Data Mining Primitive Computation. IEEE International Conference on Peer-to-Peer Computing, Seattle. pp. 212-221. 2009. (Invited for a fast track submission to Springer Journal on Peer-to-Peer Networking and Applications (PPNA), as one of the outstanding papers of P2P'09). pdf

4.  C. Giannella, K. Liu, and H. Kargupta. (2008). On the Privacy of Euclidean Distance Preserving Data Perturbation. IEEE Transactions on Knowledge and Data Engineering. (In communication). Abstract

5.      K. Liu, C. Giannella, and H. Kargupta. (2008). A survey of attack techniques on privacy-preserving data perturbation methods. In Privacy-Preserving Data Mining: Models and Algorithms. Chapter 15, pages 357-380. Edited by Charu Aggarwal and Philip S Yu, February 2008 Abstract pdf

6.      K. Liu, K. Das, T. Grandison, and H. Kargupta. (2008). Privacy-Preserving Data Analysis on Graphs and Social Networks. In Next Generation Data Mining. Edited by Hillol Kargupta, Jiawei Han, Philip Yu, Rajeev Motwani, and Vipin Kumar, CRC Press. Abstract pdf

7.      H. Kargupta, K. Das, and K. Liu. (2007). A Game Theoretic Approach toward Multi-Party Privacy-Preserving Distributed Data Mining. 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Polland, September 2007. Abstract pdf (PKDD'07 shorter version) pdf (longer version)

8.      K. Liu, H. Kargupta, and J. Ryan. (2006) Random projection-based multiplicative
data perturbation for privacy preserving distributed data mining. IEEE
Transactions on Knowledge and Data Engineering (TKDE), volume 18, number 1, pp. 92-106, January 2006. Abstract pdf

9.      K. Liu, C. Giannella, and H. Kargupta. (2006). An attacker's view of distance preserving maps for privacy preserving data mining. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), Berlin, Germany, September 2006. Abstract pdf

10.      H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. (2005). Random Data Perturbation Techniques and Privacy Preserving Data Mining. Knowledge and Information Systems Journal, volume 7, number 4, pp. 387--414. (A paper on privacy preserving data mining and random data perturbation) pdf

11. D. Meng, K. Sivakumar, and H. Kargupta. (2004). Privacy Sensitive Bayesian Network Parameter Learning. Proceedings of the Fourth IEEE International Conference on Data Mining. Brighton , UK , pp. 427-430. (This paper describes a privacy-preserving algorithm for learning parameters of a Beysian network from distributed data.) pdf short version

12.      H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. (2003). On the Privacy Preserving Properties of Random Data Perturbation Techniques. Proceedings of the IEEE International Conference on Data Mining. Melbourne , Florida , USA , pp. 99—106. (Winner of 2003 IEEE Data Mining Conference Best Paper Award).  Abstract pdf

13.      H. Kargupta, K. Liu, and J. Ryan. (2003). Privacy sensitive distributed data mining from multi-party data. In Proceedings of the First NSF/NIJ Symposium on Intelligence and Security Informatics, Lecture Notes in Computer Science, pp. 336-342, Tucson, AZ, June 2003. Springer Berlin/Heidelberg. Abstract pdf

14.      H. Kargupta, H. Dutta, S. Datta , K. Sivakumar (2003) Privacy Preserving Data Mining and Random Perturbation. Proceedings of the Workshop on Privacy in the Electronic Society (WPES'03), Washington  DC,October,2003 Abstract pdf

15.      H. Kargupta, K. Liu, S. Datta, J. Ryan, K. Sivakumar(2003) Link Analysis, Privacy Preservation, and Random Perturbations.  Proceedings of the  KDD Workshop on Link Analysis for Detecting Complex Behavior  (LinkKDD’03), Washington D.C., July  2003
pdf

16.  H. Kargupta, K. Liu, S. Datta, J. Ryan, and K. Sivakumar. (2003). Homeland security and privacy sensitive data mining from multi-party distributed resources. In Proceedings of the 12th IEEE International Conference on Fuzzy Systems, volume 2, pp. 1257-1260, St. Louis, MO, May 2003. Abstract pdf

17.  S. Datta, H. Kargupta and K. Sivakumar. (2003) .Homeland Defense, Privacy-Sensitive Data Mining, and Random Value Distortion .  Proceedings of the  SIAM Workshop on Data Mining for Counter Terrorism and Security  (SDM'03),San Francisco, CA, May, 2003.
pdf

18.  H. Kargupta, K. Liu, and J. Ryan. Random projection and privacy preserving correlation computation from distributed data. In Proceedings of the 6th International Workshop on High Performance Data Mining: Pervasive and Data Stream Mining (HPDM:PDS'03). Held In conjunction with the third International SIAM Conference on Data Mining (SDM'03), San
Francisco, CA, May 2003

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Computation in Gene Expression

1.      H. Kargupta, R. Ayyagari, and S. Ghosh. (2003). Learning Functions Using Randomized Expansions: Probabilistic Properties and Experimentations. IEEE Transactions on Knowledge and Data Engineering, Volume 16, Number 8, pp. 894-908. (A paper on probablistic properties of randomized genetic code-like transformations) pdf

2.      H. Kargupta and S. Ghosh. (2002). Towards Machine Learning using Genetic Code-like Transformations. Genetic Programming and Evolvable Machine Journal, Volume 3, no. 3, pp. 231-258. pdf

  1. R. Ayyagari, H. Kargupta (2002). A Resampling Technique for Learning the Fourier Spectrum of Skewed Data. Proceedings of the 7th Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM SIGMOD 2002. Pp. 39--44. (A paper on a resampling based approach for estimating multi-variate Fourier spectrum of discrete functions from non-uniform distribution.) pdf
  2. H. Kargupta. (2000). A Striking Property of Genetic Code-like Transformations. Complex System Journal. Vol. 13, no. 1, pp. 1--32. pdf
  3. H. Kargupta. (1997). SEARCH, Computational Processes in Evolution, and Preliminary Development of the Gene Expression Messy Genetic Algorithm. Complex Systems, Volume 11, issue 4, pp. 233-287.   
  4. H. Kargupta and K. Buescher (1995). The Gene Expression Messy Genetic Algorithm For Financial Applications. Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering. IEEE Press. 155--161.
  5. H.Kargupta and B. Park (2001). Gene Expression and Fast Construction of Distributed Evolutionary Representation. Journal of Evolutionary Computation. Vol. 9, no. 1, pp. 1--32, MIT Press. pdf

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Genetic Algorithms

  1. H. Kargupta (1999). Scalar Evolutionary Computation. Guest editorial in the Journal of Evolutionary Computation. vol 7. no 4. iii--iv. MIT Press. ps
     
  2. H. Kargupta and K. Sarkar (1999). Function Induction, Gene Expression, And Evolutionary Representation Construction.   Proceedings of the Genetic and Evolutionary Computation Conference. vol 1. 313--320. AAAI Press.
     
  3. H. Kargupta and B. Park (1999). Fast Construction of Distributed and Decomposed Evolutionary Representation. Late Breaking Proceedings of the Genetic and Evolutionary Computation Conference. 139--148. AAAI Press.
     
  4. H. Kargupta and S. Bandyopadhyay (1999).   A Perspective On The Foundation And Evolution Of The Linkage Learning Genetic Algorithms. Special Issue on Genetic Algorithms, the Journal of Computer Methods in Applied Mechanics and Engineering (CMAME). Guest Eds: David E. Goldberg and Kalyanmoy Deb. 186 (2000), 266--294. ps.gz pdf
     
  5. H. Kargupta, B. Park, E. Johnson, E. Riva Sanseverino, L. D. Silvestre and D. Hershberger (1998). Scalable Data Mining From Distributed, Heterogeneous Data, Using Collective Learning and Gene Expression Based Genetic Algorithms. Workshop on distributed data mining, International Conference on Knowledge Discovery and Data Mining. New York , NY , USA . School of EECS, Washington State University Technical Report, EECS-98-001.
     
  6. H. Kargupta, E. Riva Sanseverino, E. Johnson, and S. Agrawal (1998) The Genetic Algorithms, Linkage Learning, And Scalable Data Mining. Intelligent Data Analysis in Science: A Handbook. Ed. Cartwright, H. Oxford University Press.
     
  7. H. Kargupta, and S. Bandyopadhyay (1998). Further Experimentation on the Scalability of the GEMGA. Lecture Notes in Computer Science. Vol. 1498. 315-324. Springer Verlag. ps.Z
     
  8. A. Campoccia, M. Silvestre, L. Dusonchet, A. Augugliaro, E. Sanseverino, and H. Kargupta. (1998). An Evolutionary Approach for Fault Diagnosis in MV Radial Distribution Networks. Submitted to the 13th Power Systems Computation Conference. Trondheim, Norway . June 28 - July 2nd, 1999.
     
  9. H. Kargupta (1998). Gene Expression And Large Scale Evolutionary Optimization. Computational Aerosciences in the 21st Century. Kluwer Academic Publishers.
     
  10. S. Bandyopadhyay, H. Kargupta, G. Wang (1998).   Revisiting The GEMGA: Scalabe Evolutionary Optimization Through Linkage Learning. Proceedings of the IEEE International Conference on Evolutionary Computation. IEEE Press. 603-608. pdf
     
  11. H. Kargupta (1997). SEARCH, Computational Processes in Evolution, and Preliminary Development of the Gene Expression Messy Genetic Algorithm. Journal of Complex Systems. Volume 11, issue 4, pp 233-287. ps.gz pdf file
     
  12. H. Kargupta (1997).Relation Learning in Gene Expression: Introns, Variable Length Representation, and All That. Research Note. Presented in the Workshop on Exploring Non-Coding Segments and Genetic-Based Encodings. International Conference On Genetic Algorithms, Michigan . ps
     
  13. H. Kargupta (1996). Computational Processes Of Evolution: The SEARCH Perspective. Presented in SIAM Annual Meeting, 1996 as the winner of the 1996 SIAM Annual Best Student Paper Prize.
     
  14. H. Kargupta (1996c). Blackbox Optimization And Messy Genetic Algorithms: Recent Developments. NATO ASI Series, Kluer Publishers. LA-UR-96-2412.
     
  15. H. Kargupta (1996d). Genetic Algorithms. Handbook of Mathematics. Kluer Publishers.
     
  16. H. Kargupta (1996). The Gene Expression Messy Genetic Algorithm. Proceedings of the 1996 IEEE International Conference on Evolutionary Computation. Nagoya University, Japan . 631--636. pdf
     
  17. H. Kargupta (1996). Performance Of The Gene Expression Messy Genetic Algorithm On Real Test Functions. Proceedings of the IEEE International Conference on Evolutionary Computation. Nagoya University, Japan . IEEE Press. 631--636. LA-UR-96-4469.
     
  18. H. Kargupta (1996). SEARCH And A Computational Perspective Of Evolution. Proceedings of the Artificial Life V, 1996, Nara, Japan . 56--63.
     
  19. D. E. Goldberg , E. Cantupaz, H. Kargupta, and J. Horn (1996). Towards A Theory Of Deme Sizing For Multiple-Run genetic Algorithms. Submitted in Foundation of Genetic Algorithms (FOGA'96). Illinois Genetic Algorithm Laboratory Technical Report Number 96002.
     
  20. H. Kargupta (1995). Signal-to-noise, Crosstalk and Long Range Problem Difficulty in Genetic Algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms.Morgan Kaufmann Publishers. pp. 193--200.
     
  21. D. E. Goldberg, K. Deb, H. Kargupta, H. George (1993). Rapid Accurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms. Proceedings of The Fifth International Conference On Genetic Algorithms. Morgan Kaufmann Publishers. 56-64. ps
     
  22. H. Kargupta (1993). Information Transmission in Genetic Algorithm and Shannon 's Second Theorem. Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann Publishers. 640.
     
  23. H. Kargupta (1992). Drift, Diffusion and Boltzmann Distribution in Simple Genetic Algorithm. Proceedings of the Workshop on Physics and Computation. IEEE Computer Society Press. 137-145.
     
  24. H. Kargupta, K. Deb, D. Goldberg, D. (1992). Ordering Genetic Algorithms and Deception. Parallel ProblemSolving from Nature, 2. (1992). Eds: Manner, R. & Manderick, B. Elsevier Science Publishers. 47-56. ps

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Theoretical Issues In Blackbox Optimization

  1. H. Kargupta, D.E. Goldberg (1996). SEARCH, Blackbox Optimization, And Sample Complexity. Foundation of Genetic Algorithms (FOGA'96). Morgan Kaufmann. ps
     
  2. H. Kargupta (1996b). Blackbox and Non-blackbox Optimization: A Common Ground. NATO ASI Series, Kluer Publishers. LA-UR-96-2410.
     
  3. H. Kargupta, D. E. Goldberg (1995). Polynomial Complexity Blackbox Optimization: The SEARCH Perspective. Proceedings of the IEEE International Conference on Evolutionary Computation. Nagoya University, Japan . IEEE Press. 792--797.
     
  4. V. Hanagandi, H. Kargupta (1996). Unconstrained Blackbox Optimization: The SEARCH Perspective. Accepted in Institute for Operations Research and the Management Sciences (INFORMS)'96. November. Atlanta .

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Decision Support Systems

1.      F. Grobler, C. Subick, H. Kargupta, K. Govindan (1994). Optimization of Uncertain Resource Allocation with Genetic Algorithm. Presented in Second ASCE Congress for Computing in Civil Engineering , Atlanta , GA. June, 1995. 

2.      B. Sahay, H. Kargupta (1990). An Expert System for Solving Partial Differential Equations. Proceedings of the International Conference for Computer Aided Science and Technology, Barcelona , Spain . Also available as M.Tech Thesis from Indian Institute of Technology, Kanpur , India .

Neural Networks/ Reaction-Diffusion Systems

  1. S. Ray, H. Kargupta (1996). A Temporal Sequence Processor Based on the Biological Reaction-Diffusion Proces. Complex Systems, Volume 9, issue 4. ps
     
  2. H. Kargupta, S. Ray (1994). Temporal Sequence Processing Based on the Biological Reaction-Diffusion Process. Proceedings of the IEEE World Congress on Computational Intelligence. Volume 4, 1994 (ICNN '94). Piscataway , NJ : IEEE Service Center , 2315-2320.
     
  3. H. Kargupta, R. Smith (1991). System Identification Using Evolving Polynomial Network. Proceedings of the Fourth International Conference on Genetic Algorithms. Eds: Belew, R. & Booker, L. Morgan Kaufmann Publishers. 370-376.

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Other Publications:

 

Sample Technical Reports

1.      H. Kargupta (1995). SEARCH, Polynomial Complexity, and The Fast Messy Genetic Algorithm. Ph.D. Dissertation, Department of Computer Science, University of Illinois at Urbana-Champaign. (Also available as IlliGAL Tech. Report 95008). ps

2.      D. E. Goldberg, H. Kargupta, J. Horn, E. Cantupaz (1995). Optimal Deme Size for Serial and Parallel Genetic Algorithms. IlliGAL Report No. 95002. Dept. of General Engineering, Univ. of Illinois at Urbana-Champaign, Urbana , Illinois , USA .

3.      H. Kargupta, D.E. Goldberg (1995). Los Alamos National Laboratory Report Number LA-UR-96-64.

4.      H. Kargupta, D.E. Goldberg (1995). Blackbox Optimization: Implications Of SEARCH. Los Alamos National Laboratory Report Number LA-UR-96-63.

5.      S. Ray,H. Kargupta(1993). A Temporal Sequence Processor based on the Biological Reaction-Diffusion Process. Technical Report Number. UIUCDCS-R-93-1842. Dept. of Computer Science, Univ. of Illinois at Urbana-Champaign, Urbana , Illinois , USA .

6.      H. Kargupta, D. E. Goldberg (1994). Decision Making in Genetic Algorithms: signal-to-noise perspective. IlliGAL Report No. 94004. Dept. of General Engineering, Univ. of Illinois at Urbana-Champaign, Urbana , Illinois , USA .

7.      H. Kargupta, K. Deb, D.E. Goldberg (1994). Simultaneous Target Tracking using Fast Messy Genetic Algorithms. IlliGAL Report No. 94008. Dept. of General Engineering, Univ. of Illinois at Urbana-Champaign, Urbana , Illinois , USA .

8.      H. Kargupta (1993). Information Transmission in Genetic Algorithm and Shannon 's Second Theorem. IlliGAL Report No. 93003. Dept. of General Engineering, Univ. of Illinois at Urbana-Champaign, Urbana , Illinois , USA .

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Unpublised Manuscripts

  1. H. Kargupta (1994). Reversible Computation and Genetic Crossover. Dept. of Computer Science, Univ. of Illinois at Urbana-Champaign, Urbana, Illinois .

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