Computational Theory Perception (CTP), rough-fuzzy uncertainty analysis and mining in bioinformatics and web intelligence: A unified framework

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5946 LNCS, Issue , 2010, Pages 106-129

  Pal, Sankar K ^a  

^a INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Bioinformatics; Case based reasoning; Data mining; Fuzzy granulation; MR image segmentation; Rough fuzzy computing; Soft computing; Web service classification

Indexed keywords

BRAIN MR; CASE BASED REASONING SYSTEMS; COMPUTATION TIME; COMPUTATIONAL THEORIES; ENSEMBLE CLASSIFIERS; FUZZY C MEAN; FUZZY CASE; FUZZY COMPUTING; FUZZY GRANULATION; FUZZY INTEGRATIONS; FUZZY UNCERTAINTIES; GRANULAR COMPUTATION; HUMAN INTELLIGENCE; MEDOIDS; MR IMAGES; NEW DIRECTIONS; PROTEIN SEQUENCES; QUANTITATIVE MEASURES; ROUGH SET; ROUGH-FUZZY CLUSTERING; ROUGH-FUZZY COMPUTING; UNIFIED FRAMEWORK; WEB INTELLIGENCE;

BIOINFORMATICS; COMPUTATION THEORY; COMPUTER AIDED DIAGNOSIS; DIGITAL IMAGE STORAGE; FUZZY CLUSTERING; FUZZY SYSTEMS; GRANULATION; IMAGE SEGMENTATION; KNOWLEDGE BASED SYSTEMS; ROUGH SET THEORY; SOFT COMPUTING; UNCERTAINTY ANALYSIS; WEB SERVICES;

CASE BASED REASONING;

EID: 77949848478     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-11479-3_7     Document Type: Conference Paper

References (47)

1. Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)
2. Banerjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Trans. Neural Networks 9, 1203-1216 (1998)
3. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum, New York (1981)
4. Dayhoff, M.O., Schwartz, R.M., Orcutt, B.C.: A Model of Evolutionary Change in Proteins. Matrices for Detecting Distant Relationships, Atlas of Protein Sequence and Structure 5, 345-358 (1978)
5. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2005)
6. http://www.daviddlewis.com/resources/testcollections/reuters21578/
7. http://www.cs.cmu.edu/~WebKB/
8. http://www.dmoz.org/
9. http://www.looksmart.com
10. Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)
11. Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low complexity fuzzy relational clustering algorithms for web mining. IEEE Trans. on Fuzzy System 9, 595-607 (2001)
12. Kuipers, B.J.: Qualitative Reasoning. MIT Press, Cambridge (1984)
13. Li, Y., Shiu, S.C.K., Pal, S.K.: Combining feature reduction and case selection in building CBR classifiers. IEEE Trans. on Knowledge and Data Engineering 18, 415-429 (2006)
14. Lingras, P., West, C.: Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems 23, 5-16 (2004)
15. Maji, P., Pal, S.K.: Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans. on System, Man and Cybernetics, Part B, 37, 1529-1540 (2007)
16. Maji, P., Pal, S.K.: Rough-fuzzy C-medoids algorithm and selection of bio-basis for amino acid sequence analysis. IEEE Trans. Knowledge and Data Engineering 19, 859-872 (2007)
17. Maji, P., Pal, S.K.: Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces. IEEE Trans. Knowledge and Data Engineering (to appear)
18. Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics 36, 795-805 (2006)
19. Mitra, S., De, R.K., Pal, S.K.: Knowledge Based Fuzzy MLP for Classification and Rule Generation. IEEE Trans. Neural Networks 8, 1338-1350 (1997)
20. National Center for Biotechnology Information, http://www.ncbi.nlm.nih. gov
21. Pal, S.K., Talwar, V., Mitra, P.: Web mining in soft computing framework: Relevance, state of the art and future directions. IEEE Trans. Neural Networks 13, 1163-1177 (2002)
22. Pal, S.K., Bandyopadhyay, S., Ray, S.S.: Evolutionary Computation in Bioinformatics: A Review. IEEE Transactions on Systems, Man, and Cybernetics, Part-C 36, 601-615 (2006)
23. Pal, S.K., Skowron, A. (eds.): Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, Singapore (1999)
24. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-neuro Computing: A Way to Computing with Words. Springer, Berlin (2003)
25. Pal, S.K., Skowron, A. (eds.): Special issue on Rough Sets, Pattern Recognition and Data Mining. Pattern Recognition Letters 24 (2003)
26. Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.): Soft Computing in Case Based Reasoning. Springer, London (2001)
27. Pal, S.K., Shiu, S.C.K.: Foundations of Soft Case Based Reasoning. John Wiley, NY (2003)
28. Pal, S.K., Mitra, P.: Case generation using rough sets with fuzzy discretization. IEEE Trans. Knowledge and Data Engineering 16, 292-300 (2004)
29. Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman & Hall CRC Press, Boca Raton (2004)
30. Pal, S.K., Ghosh, A., Sankar, B.U.: Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. International Journal of Remote Sensing 2, 2269-2300 (2000)
31. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing Techniques for Computing with Words. Springer, Berlin (2004)
32. Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.): PReMI 2005. LNCS, vol. 3776. Springer, Heidelberg (2005)
33. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)
34. Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley, N.Y. (2008)
35. Ray, S.S., Bandyopadhyay, S., Mitra, P., Pal, S.K.: Bioinformatics in Neurocomputing Framework. IEE Proc. Circuits Devices & Systems 152, 556-564 (2005)
36. Saha, S., Murthy, C.A., Pal, S.K.: Rough set Based Ensemble Classifier for Web Page Classification. Fundamentae Informetica 76, 171-187 (2007)
37. Saha, S., Murthy, C.A., Pal, S.K.: Classification of Web Services using Tensor Space Model and Rough Ensemble Classifier. In: Proc. 17th International Symposium on Methodologies for Intelligent Systems, Toronto, Canada, pp. 508-513 (2008)
38. Sen, D., Pal, S.K.: Histogram Thresholding using Fuzzy and Rough Measures of Association Error. IEEE Trans. Image Processing 18, 879-888 (2009)
39. Sen, D., Pal, S.K.: Generalized Rough Sets, Entropy and Image Ambiguity Measures. IEEE Trans. Syst, Man and Cyberns. Part B 39, 117-128 (2009)
40. Sun, R.: Integrating Rules and Connectionism for Robust Commonsense Reasoning. Wiley, N.Y. (1994)
41. Swiniarski, R.W., Skowron, A.: Rough SetMethods in Feature Selection and Recognition. Pattern Recognition Letters 24, 833-849 (2003)
42. Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.): RSKT 2008. LNCS (LNAI), vol. 5009. Springer, Heidelberg (2008)
43. Yao, Y.Y.: Granular Computing: Basic Issues and Possible Solutions. In: Proceedings of the 5th Joint Conference on Information Sciences, vol. I, pp. 186-189 (2000)
44. Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22, 73-84 (2001)
45. Zadeh, L.A., Pal, S.K., Mitra, S.: Foreword. In: Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. Wiley, New York (1999)
46. Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. ACM 37, 77-84 (1994)
47. Zadeh, L.A.: Towards a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets Systems 19, 111-127 (1997)