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Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism

하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출

  • Kim, Jin-Sung (School of Business Administration, Jeonju University)
  • Published : 2004.10.01

Abstract

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

References

  1. Agrawal, R., Imielinski T., and Swami, A. (1993), Mining Association Rules between Sets of Items in large Databases, In Proc. of the ACM SIGMOD Conference on Management of Data, Washington, D.C., 207-216.
  2. Bonchi, F., Giannotti, F., Gozzi, C., Manco, G., Nanni, M., Pedreschi, D., Renso, C., and Ruggieri, S. (2001), Web Log Data Warehousing and Mining for Intelligent Web Caching, Data & Knowledge Engineering, 39, 165-189. https://doi.org/10.1016/S0169-023X(01)00038-6
  3. Chakrabarti, S., Dom, B.E., Kumar, S. R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D., and Kleinberg, J.M. (1999), Mining the Web's Link Structure, Computer, 32, 60-67.
  4. Changchien, S.W., and Lu, T.C. (2001), Mining Association Rule Procedure to Support On-Line Recommendation by Customers and Products Fragmentation, Expert Systems with Applications, 20, 325-335. https://doi.org/10.1016/S0957-4174(01)00017-3
  5. Hong, T.P., Lin, K.Y., and Wang, S.L. (2003), Fuzzy Data Mining for Interesting Generalized Association Rules, Fuzzy Sets and Systems, 138(2), 255-269. https://doi.org/10.1016/S0165-0114(02)00272-5
  6. Horikawa, S.I., Furuhashi, T., and Uchikawa, Y. (1992), On Fuzzy Modeling using Fuzzy Neural Networks with the Backpropagation Algorithm, IEEE Transactions on Neural Networks, 3(5), 801-806. https://doi.org/10.1109/72.159069
  7. Hui, S.C. and Jha, G.. (2000), Data Mining for Customer Service Support, Information & Management, 38, 1-13. https://doi.org/10.1016/S0378-7206(00)00051-3
  8. Krishnapuram, R. and Lee, J. (1992), Fuzzy-Connective-Based Hierarchical Aggregation Networks for Decision Making, Fuzzy Sets and Systems, 46(1), 11-27. https://doi.org/10.1016/0165-0114(92)90263-4
  9. Lee, K.C., Kim, J.S., Chung, N.H., and Kwon, S.J. (2002), Fuzzy Cognitive Map Approach to Web-mining Inference Amplification, Expert Systems with Applications, 22, 197-211. https://doi.org/10.1016/S0957-4174(01)00054-9
  10. Lin, C.T. and Lee, C.S.G. (1991), Neural- network-based Fuzzy Logic Control and Decision System, IEEE Transactions on Computer, C-40 (12), 1320-1336.
  11. Mitra, S. and Pal, S.K. (1994), Logical Operation Based Fuzzy MLP for Classification and Rule Generation, Neural Networks, 7(2), 353-373. https://doi.org/10.1016/0893-6080(94)90029-9
  12. Shann, J.J. and Fu, H.C. (1995), A Fuzzy Neural Network for Rule Acquiring on Fuzzy Control Systems, Fuzzy Sets and Systems, 71, 345-357. https://doi.org/10.1016/0165-0114(94)00277-E
  13. Song, H.S., Kim, J.K., and Kim, S.H. (2001), Mining the Change of Customer Behavior in an Internet Shopping Mall, Expert Systems with Applications, 21, 157-168. https://doi.org/10.1016/S0957-4174(01)00037-9
  14. Kong, S.G. and Kosko, B. (1992), Adaptive Fuzzy Systems for Backing up a Truck-and-Trailer, IEEE Transactions on Neural Networks, 3(2), 211-223. https://doi.org/10.1109/72.125862
  15. Kosko, B. (1992), Neural Networks and Fuzzy Systems: A Dynamic Systems Approach to Machine Intelligence, Prentice-Hall, Englewood Cliffs, NJ.
  16. Krishnapuram, R. and Lee, J. (1992), Fuzzy-set-based Hierarchical Networks for Information Fusion in Computer Vision, Neural Networks, 5, 335-350. https://doi.org/10.1016/S0893-6080(05)80031-0
  17. Wang, L.X. and Mendel, J.M. (1992), Back-propagation Fuzzy Systems as Nonlinear Dynamic System Identifiers, IEEE International Conference on Fuzzy Systems, 1409-1418.