Multi-granule Association Rule Mining Based on Quantitative Concept Lattice

Author:

Wang Dexing,Xie Qian,Huang Dongmei,Yuan Hongchun,Lu Hongyan,Xu Jielong

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. Han, J., Cai, Y., Cercone, N.: Knowledge discovery in databases: An attribute-oriented approach. In: Proceedings of the18th VLDB Conference, Vancouver, British Columbia, Canada, pp. 547–559 (1992)

2. Han, J., Cai, Y., Cercone, N.: Data-driven discovery of quantitative rules in relation databases. IEEE Transactions on Knowledge and Data Engineering 5(1), 29–40 (1993)

3. Carter, C.L., Hamilton, H.J.: Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases. In: Proceedings of 7th IEEE International Conference Tools with Artificial Intelligence, Washington, DC, pp. 486–489 (1995)

4. Carter, C.L., Hamilton, H.J.: Efficient attribute-oriented generalization for knowledge discovery from large databases. IEEE Transactions on Knowledge and Data Engineering 10(2), 193–208 (1998)

5. Chen, Y.L., Shen, C.C.: Mining generalized knowledge from ordered data through attribute-oriented induction techniques. European Journal of Operational Research 166(1), 221–245 (2005)

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