اخبار و اعلانات
آمار
| تعداد نشریات | 27 |
| تعداد شماره‌ها | 565 |
| تعداد مقالات | 5,820 |
| تعداد مشاهده مقاله | 8,141,792 |
| تعداد دریافت فایل اصل مقاله | 5,450,371 |
DISTRIBUTED AND COLLABORATIVE FUZZY MODELING | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 2، دوره 4، شماره 1، تیر 2007، صفحه 1-19 اصل مقاله (475.96 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2007.353 | ||
| نویسنده | ||
| WITOLD PEDRYCZ | ||
| DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING, UNIVERSITY OF ALBERTA, EDMONTON T6R 2G7 CANADA AND SYSTEMS RESEARCH INSTITUTE OF THE POLISH ACADEMY OF SCIENCE, WARSAW, POLAND | ||
| چکیده | ||
| In this study, we introduce and study a concept of distributed fuzzy modeling. Fuzzy modeling encountered so far is predominantly of a centralized nature by being focused on the use of a single data set. In contrast to this style of modeling, the proposed paradigm of distributed and collaborative modeling is concerned with distributed models which are constructed in a highly collaborative fashion. In a nutshell, distributed models reconcile and aggregate findings of the individual fuzzy models produced on a basis of local data sets. The individual models are formed in a highly synergistic, collaborative manner. Given the fact that fuzzy models are inherently granular constructs that dwell upon collections of information granules – fuzzy sets, this observation implies a certain general development process. There are two fundamental design issues of this style of modeling, namely (a) a formation of information granules carried out on a basis of locally available data and their collaborative refinement, and (b) construction of local models with the use of properly established collaborative linkages. We discuss the underlying general concepts and then elaborate on their detailed development. Information granulation is realized in terms of fuzzy clustering. Local models emerge in the form of rule-based systems. The paper elaborates on a number of mechanisms of collaboration offering two general categories of so-called horizontal and vertical clustering. The study also addresses an issue of collaboration in cases when such interaction involves information granules formed at different levels of specificity (granularity). It is shown how various algorithms of collaboration lead to the emergence of fuzzy models involving information granules of higher type such as e.g., type-2 fuzzy sets. | ||
| کلیدواژه‌ها | ||
| Computational Intelligence؛ C^{3} paradigm؛ Distributed processing؛ Fuzzy clustering؛ Fuzzy models | ||
| مراجع | ||
|
[1] R. Agarwal and R. Srikant, Privacy-preserving data mining., Proc. of the ACM SIGMOD Conference on Management of Data, ACM Press, New York, May (2000), 439–450. [2] A. M. Bensaid, L. O. Hall, J. C. Bezdek and L. P. Clarke. Partially supervised clustering for image segmentation, Pattern Recognition, 29(5) (1996), 859-871. [3] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, NY. (1981) [4] C. Clifton and D. Marks, Security and privacy implications of data mining, Workshop on Data Mining and Knowledge Discovery, Montreal, Canada, (1996), 15–19. [5] J. C. Da Silva, C. Giannella, R. Bhargava, H. Kargupta and M. Klusch, Distributed data mining and agents, Engineering Applications of Artificial Intelligence, 18 (7) (2005), 791- 807. [6] W. Du and Z. Zhan, Building decision tree classifier on private data, Clifton, C., Estivill- Castro, V. (Eds.), IEEE ICDM Workshop on Privacy, Security and Data Mining, Conferences in Research and Practice in Information Technology, Vol. 14, Maebashi City, Japan, ACS, (2002), 1–8. [7] T. Johnsten and V. V. Raghavan, A methodology for hiding knowledge in databases, Clifton, C., Estivill-Castro, C. (Eds.), IEEE ICDM Workshop on Privacy, Security and Data Mining, Conferences in Research and Practice in Information Technology, Vol. 14. Maebashi City, Japan, ACS, (2002), 9–17. [8] H. Kargupta, L. Kun, S. Datta, J. Ryan and K. Sivakumar, Homeland security and privacy sensitive data mining from multi-party distributed resources, Proc. 12th IEEE International Conference on Fuzzy Systems, FUZZ '03, .Volume 2, May (2003), 25-28, Vol. 2 (2003), 1257 – 1260. [9] S. Merugu, and J. Ghosh, A privacy-sensitive approach to distributed clustering, Pattern Recognition Letters, 26 (4) (2005), 399-410. [10] B. Park and H. Kargupta, Distributed data mining: algorithms, systems, and applications, In: Ye, N. (Ed.), The Handbook of Data Mining. Lawrence Erlbaum Associates, New York, (2003), 341–358. [11] W. Pedrycz, Algorithms of fuzzy clustering with partial supervision, Pattern Recognition Letters, 3 (1985), 13 - 20. [12] W. Pedrycz, and J. Waletzky, Fuzzy clustering with partial supervision, IEEE Trans. on Systems, Man and Cybernetics, 5 (1997), 787-795. [13] W. Pedrycz and J. Waletzky, Neural network front-ends in unsupervised learning, IEEE Trans. on Neural Networks, 8 (1997), 390-401. [14] W. Pedrycz, V. Loia and S. Senatore, P-FCM: A proximity-based clustering, Fuzzy Sets & Systems, 148, (2004), 21-41. [15] W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognition Letters, 23(14)(2002), 1675-1686. [16] W. Pedrycz, Knowledge-Based Clustering: From Data to Information Granules, J. Wiley, New York (2005). [17] W. Pedrycz and F. Gomide, Fuzzy Systems Engineering: Toward Human-Centric Computing, J. Wiley, NJ Hoboken, ( 2007). [18] A. Strehl and J. Ghosh, Cluster ensembles—a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, 3, (2002), 583–617. [19] H. Timm, F. Klawonn and R. Kruse, An extension of partially supervised fuzzy cluster analysis, Proc. Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS, (2002), 63 –68. [20] G. Tsoumakas, L. Angelis and I. Vlahavas, Clustering classifiers for knowledge discovery from physically distributed databases, Data & Knowledge Engineering, 49(3) (2004), 223- 242. [21] V. S. Verykios, et al. State of the art in privacy preserving data mining, SIGMOID Record, 33(1) (2004), 50-57. [22] L. A. Zadeh, Toward a generalized theory of uncertainty (GTU) – an outline, Information Sciences, 172(1-2) (2005), 1-40. | ||
|
آمار تعداد مشاهده مقاله: 2,181 تعداد دریافت فایل اصل مقاله: 1,645 |
||
