Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 8089–8098 | Cite as

Association rules redundancy processing algorithm based on hypergraph in data mining

  • Maozhu Jin
  • Hua Wang
  • Qian ZhangEmail author
Article

Abstract

In order to achieve the research from individual data to data system and from passive verification of data to active discovery, taking high dimensional data oriented data mining technology as the research object, an association rule redundancy processing algorithm based on hypergraph in data mining technology is studied according to the project requirements. The concepts of hypergraph and system are introduced to explore the construction of hypergraph on 3D matrix model. In view of the characteristics of big data, a new method of super edge definition is adopted, which improves the ability of dealing with problems. In the association rules redundancy and loop detection based on directed hypergraph, the association rules are transformed into directed hypergraph, and the adjacency matrix is redefined. The detection of redundancy and loop is transformed into the processing of connected blocks and circles in hypergraph, which provides a new idea and method for the redundant processing of association rules. The new method is applied to the data processing of practical projects. The experimental results show that the 3D matrix mathematical model and related data mining algorithms in this paper can find new high-quality knowledge from high-dimensional data.

Keywords

Data mining Hypergraph Association rules Redundant processing Smart economy Business intelligence 

Notes

Acknowledgements

This work was supported by The National Natural Science Foundation of China (Grant Nos. 71001075 and 61471090), and the Fundamental Research Funds for the Central Universities (Grant No. skqy201739).

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 5.
    Henriques, R., Antunes, C., Madeira, S.C.: A structured view on pattern mining-based biclustering. Pattern Recognit. 48(12), 3941–3958 (2015)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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. 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)CrossRefGoogle Scholar
  22. 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 CrossRefGoogle Scholar
  23. 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 CrossRefGoogle Scholar
  24. 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 MathSciNetCrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business School of Sichuan UniversityChengduChina
  2. 2.Economic and Management SchoolChengdu Agricultural CollegeChengduChina

Personalised recommendations