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The application of agglomerative hierarchical spatial clustering algorithm in tea blending

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Abstract

Playing a significant role in production process of tea enterprise, tea blending has advantages in improving the quality of tea, expanding resource of tea, and getting higher profits. Traditional manual blending method not merely wastes time and energy but also has difficulties in optimizing blending programs. In this paper, tea blending problem is modeled as spatial clustering based on multi-dimensional hierarchy space. Tea attributes such as varieties of tea bush, process crafts and producing areas are modeled into hierarchy space with tree structure. Every node of these trees is present a value of tea attributes. So all the data tuples are mapped as points in multi-dimensional hierarchy space. And we define a similarity-based measure criterion in multi-dimensional conceptual layered space to present an agglomerative hierarchical spatial clustering based algorithm to work out the optimal blending program. Meanwhile, Dewey code is introduced to increase resolution efficiency. Dewey code is used presented the points in hierarchy space, and the codes of every point can be adopted to compute the similarity of every two points in the measure criterion. Two clustering algorithms have been proposed. We agglomerate two points which are the closest to each other according to the similarity measure criterion in hierarchy space by algorithm AGHC. In DIHC, the whole dataset is divided into small parts through K-medios until there are N clusters and N is set by users. Tea attributes are quantified and the process of tea blending is standardized. This study enable tea blending to get rid of human experience, instead of intelligent approach. The results of tea blending in this article can be accurate due to the strict consolidation doctrine. That means we will be rational when we choose which tea to be blended in practice to satisfy customers’ demands. Appropriate tea will be blended by several kinds of tea with the study in this article. Finally, experiments on real data set demonstrate the solution of tea blending proposed in this paper is greatly improving work efficiency and economic benefits.

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References

  1. Joliffe, L.: Chapter 6: The lure of tea: history, traditions and attractions. In: Hall, C.M., Sharples, L., Mitchell, R., Macionis, N. (eds.) Food Tourism Around the World, pp. 121–136. Routledge, London (2003)

    Chapter  Google Scholar 

  2. Yongcun, Z.: Matching method and mastery of fire power of Oolong tea. China Tea Process. 3, 13–15 (1997)

    Google Scholar 

  3. Yongtai, L., Naishen, L.: Comprehensive analysis of tea sensory identification. Syst. Eng. Theory Pract. 35(6), 59–66 (1989)

    Google Scholar 

  4. Tong, H.R., Gong, Z.L.: Study on blending of made tea with mixture design. J. Tea Sci. 24(3), 207–211 (2004)

    Google Scholar 

  5. Yiping, L.: Blending techniques of finished tea baking embryo. Tea Sci. Technol. 2, 34–35 (1997)

    Google Scholar 

  6. Lu, S.: How to do a good job of finished tea match. China Tea 6, 39–40 (1981)

    Google Scholar 

  7. Xingming, H.: Blending technology of Kong Fu black tea. J. Tea Bus. 1, 35–36 (1995)

    Google Scholar 

  8. Guohai, W.: The practice of the roller blending machine in tea blending. Guangdong Tea 1, 31–32 (2003)

    Google Scholar 

  9. Hewei, G.: Design of the control system of tea evenly pushing machine based on PLC. Hubei Agric. Sci. 52(4), 933–936 (2013)

    Google Scholar 

  10. Zhiquan, W.: Application of vibrating machine in the process of tea blending. Hoisting Convey. Mach. 3, 66–67 (1981)

    Google Scholar 

  11. Hetao, Z.: Application of electronic technology in tea processing. Today Sci. Technol. 5, 9–9 (1995)

    Google Scholar 

  12. Xiangsheng, Y., Kaixin, Y.: On the mechanization of black tea blending. Tea Commun. 1, 47–48 (1990)

    Google Scholar 

  13. Tong, H.R., Gong, Z.L.: Study on blending of made tea with mixture design. J. Tea Sci. 03, 207–211 (2004)

    Google Scholar 

  14. Yiping, L., Xuemao, L.: Talking about the skills of blending the finished tea. Fujian Tea 1, 44–46 (1998)

    Google Scholar 

  15. Lingyi, L.: Discussion on tea blending. China Tea 2, 35 (1988)

    Google Scholar 

  16. Kang, C.: Key points of refining technology of high-grade refined baked green tea. Tea Sci. Technol. 3, 29–31 (1994)

    Google Scholar 

  17. Zheng, Y.: Technical measures of green tea refined baked processing of township tea factory. China Tea Process. 2, 21–22 (1994)

    Google Scholar 

  18. Qisong, S.: The design of tea blending production line of Yunnan tea export black tea. China Tea 4, 38–39 (1995)

    Google Scholar 

  19. Bingxuan, Z.: Pinions on the blending of finished tea. J. Tea Bus. 4, 32–33 (1985)

    Google Scholar 

  20. Caiyan, W., Zhuoneng, Z., Rongxi, L.: Method for matching oolong tea, CN 101897373 A (2010)

  21. Ju, C., Wang, G.: Research and implementation of PTBDSS based on knowledge. J. Comput. Res. Dev. (1998)

  22. Wensheng, X., Guangming, W., Chunhua, L.: Application of linear programming in tea blendin. Comput. Technol. Autom. 3, 61–64 (1995)

    Google Scholar 

  23. Liu, J.: Goal programming applied to seek to the best scheme of making up tea. Syst. Eng. Eng. Theory 3, 21–43 (1992)

    Google Scholar 

  24. Liu, C.: Tea blending method based on multi factor constraint, CN1742592. (2006)

  25. Liu, C., Wu, L., Wang, G.: The implementation and application of the planning model of the coordination class based on the rule and multi factor constraints. J. Ind. Eng. Manag. 15(1), 50–54 (2001)

    Google Scholar 

  26. Chakrabarti, D., Faloutsos, C.: Graph mining: laws, generators, and algorithms. ACM. Comput. Surv. 38(1), 2 (2006)

    Article  Google Scholar 

  27. Chakrabarti, D., Faloutsos, C., Zhan, Y.: Visualization of large networks with min-cut plots, a-plots and R-MAT. Int. J. Hum. Comput. Stud. 65(5), 434–445 (2007)

    Article  Google Scholar 

  28. Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)

    Article  MathSciNet  Google Scholar 

  29. Huan, J., Wang, W., Prins, J., Yang, J.: SPIN: Mining maximal frequent subgraphs from graph databases. In: Proceedings of KDD’04, pp. 581–586 (2004)

  30. Wang, W., Wang, C., Zhu, Y., Shi, B., Pei, J., Yan, X., Han, J.: GraphMiner: a structural pattern-mining system for large disk based graph databases and its applications. In: Proceedings of SIGMOD’05, pp. 879–881 (2005)

  31. Sun, J., Xie, Y., Zhang, H., Faloutsos, C.: Less is more: sparse graph mining with compact matrix decomposition. Stat. Anal. Data Min. 1(1), 6–22 (2008)

    Article  MathSciNet  Google Scholar 

  32. Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: astructural clustering algorithm for networks. In: Proceedings of KDD’07, pp 24–833 (2007)

  33. Battista, G., Eades, P., Tamassia, R., Tollis, I.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, Upper Saddle River (1999)

    MATH  Google Scholar 

  34. Herman, I., Melancon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. IEEE Trans. Vis. Comput. Graph. 6(1), 24–43 (2000)

    Article  Google Scholar 

  35. Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: Proceedings of SIGMOD’08, pp. 567–580 (2008)

  36. Tian, Y., Hankins, R., Patel, J. M.: Efficient aggregation for graph summarization. In: Proceedings of SIGMOD’08, pp. 419–432 (2008)

  37. Gil-Garcia, R., Badia-Contelles, J.M., Pons-Porrata, A.: A general framework for agglomerative hierarchical clustering algorithms. International Conference on Pattern Recognition. IEEE Computer Society, pp. 569–572 (2006)

  38. Zhang, N., Tian, Y., Patel, J.M.: Discovery-driven graph summarization. Data Engineering (ICDE) (2010)

  39. Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques. China Machine Press, Beijing (2001)

    MATH  Google Scholar 

  40. Mingxiu, D.: The Research and Application of Hierarchical Clustering Algorithm. Central South University (2009)

  41. Kim, S.: Research on the Knowledge Discovery in Conceptual Hierarchy Knowledge Base Based on the Multiple-Level Association Rules. Tianjin University, Tianjin (2006)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science, South Central University for Nationalities, Wuhan, 430000, China

    Jun Tie, Wenying Chen, Chong Sun, Tengyue Mao & Guanglin Xing

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  1. Jun Tie
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  2. Wenying Chen
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  3. Chong Sun
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  4. Tengyue Mao
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  5. Guanglin Xing
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Correspondence to Jun Tie.

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Tie, J., Chen, W., Sun, C. et al. The application of agglomerative hierarchical spatial clustering algorithm in tea blending. Cluster Comput 22 (Suppl 3), 6059–6068 (2019). https://doi.org/10.1007/s10586-018-1813-z

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