Association rules redundancy processing algorithm based on hypergraph in data mining (original) (raw)

References

  1. Li, J., Huang, L., Zhou, Y., et al.: Computation partitioning for mobile cloud computing in a big data environment. IEEE Trans. Ind. Inf. 13(4), 2009–2018 (2017)
    Article Google Scholar
  2. Wu, J.S., Guo, S., Li, J., et al.: Big data meet green challenges: big data toward green applications. IEEE Syst. J. 10(3), 888–900 (2016)
    Article Google Scholar
  3. Wu, J.S., Guo, S., Li, J., et al.: Big data meet green challenges: greening big data. IEEE Syst. J. 10(3), 873–887 (2016)
    Article Google Scholar
  4. Wei, W., Fan, X., Song, H., et al. Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Trans. Serv. Comput. (2016). https://doi.org/10.1109/TSC.2016.2528246
    Article Google Scholar
  5. Henriques, R., Antunes, C., Madeira, S.C.: A structured view on pattern mining-based biclustering. Pattern Recognit. 48(12), 3941–3958 (2015)
    Article Google Scholar
  6. Shekhar, S., Jiang, Z., Ali, R.Y., Eftelioglu, E., Tang, X., Gunturi, V., Zhou, X.: Spatiotemporal data mining: a computational perspective. ISPRS Int. J. Geo-Inf. 4(4), 2306–2338 (2015)
    Article Google Scholar
  7. Tang, G., Pei, J., Bailey, J., Dong, G.: Mining multidimensional contextual outliers from categorical relational data. Intell. Data Anal. 19(5), 1171–1192 (2015)
    Article Google Scholar
  8. Zamora, M., Baradad, M., Amado, E., Cordomí, S., Limón, E., Ribera, J., ... & Gavaldà, R. (2015). Characterizing chronic disease and polymedication prescription patterns from electronic health records. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–9. IEEE (2015)
  9. Xun, Y., Zhang, J., Qin, X., Zhao, X.: FiDoop-DP: data partitioning in frequent itemset mining on hadoop clusters. IEEE Trans. Parallel Distrib. Syst. 28(1), 101–114 (2017)
    Article Google Scholar
  10. Al-Najdi, A., Pasquier, N., Precioso, F.: Frequent closed patterns based multiple consensus clustering. In: International Conference on Artificial Intelligence and Soft Computing, pp. 14–26. Springer, Cham. (2016). https://doi.org/10.1007/978-3-319-39384-1_2
    Google Scholar
  11. Li, J., Yu, F.R., Deng, G., et al.: Industrial Internet: a survey on the enabling technologies, applications, and challenges. IEEE Commun. Surv. Tutor. 19(3), 1504–1526 (2017)
    Article Google Scholar
  12. Li, J., Zhang, S., Yang, L., et al.: Accurate RFID localization algorithm with particle swarm optimization based on reference tags. J. Intell. Fuzzy Syst. 31(5), 2697–2706 (2016)
    Article Google Scholar
  13. Li, J., He, S., Ming, Z., et al.: An intelligent wireless sensor networks system with multiple servers communication. Int. J. Distrib. Sens. Netw. 11(8), 960173 (2015)
    Article Google Scholar
  14. Wei, W., Song, H., Li, W., et al.: Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network. Inf. Sci. 408, 100–114 (2017)
    Article Google Scholar
  15. Wei, W., Sun, Z., Song, H., et al.: Energy balance-based steerable arguments coverage method in WSNs. IEEE Access. (2017). https://doi.org/10.1109/ACCESS.2017.2682845
    Article Google Scholar
  16. Wu, J.S., Blostein, S.D.: High-rate diversity across time and frequency using linear dispersion. IEEE Trans. Commun. 56(9), 1469–1477 (2008)
    Article Google Scholar
  17. Xiao, P., Wu, J.S., Cowan, C.F.N.: MIMO detection schemes with interference and noise estimation enhancement. IEEE Trans. Commun. 59(1), 26–32 (2011)
    Article Google Scholar
  18. Xiao, P., Wu, J.S., Sellathurai, M., et al.: Iterative multiuser detection and decoding for DS-CDMA system with space-time linear dispersion. IEEE Trans. Veh. Technol. 58(5), 2343–2353 (2009)
    Article Google Scholar
  19. Luo, Q.L., Fang, W., Wu, J.S., et al.: Reliable broadband wireless communication for high speed trains using baseband cloud. EURASIP J. Wirel. Commun. Netw. 2012, 1–12 (2012)
    Article Google Scholar
  20. Hu, J., Jia, S., Wu, K.: Semantic-based requirements content management for cloud software. Math. Prob. Eng. (2015). https://doi.org/10.1155/2015/474157
    Google Scholar
  21. Yang, A., Han, Y., Pan, Y., et al.: Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results Phys. 7, 1046–1050 (2017)
    Article Google Scholar
  22. Cui, K., Qin, X.: Virtual reality research of the dynamic characteristics of soft soil under metro vibration loads based on BP neural networks. Neural Comput. Appl. (2017). https://doi.org/10.1007/s00521-017-2853-7
    Article Google Scholar
  23. Sun, Y., Qiang, H., Mei, X., et al.: Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems. Neural Comput. Appl. (2017). https://doi.org/10.1007/s00521-017-2983-y
    Article Google Scholar
  24. Cui, K., Zhao, T.T.: Unsaturated dynamic constitutive model under cyclic loading. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-0881-9
    Article MathSciNet Google Scholar
  25. Cui, K., Yang, W., Gou, H.: Experimental research and finite element analysis on the dynamic characteristics of concrete steel bridges with multi-cracks. J. Vibroeng. 19(6), 4198–4209 (2017)
    Article Google Scholar

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