Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2018, 1(1); doi: 10.25236/AJCIS.010004.

Analysis of Massive Unstructured Data Model Based on Clustering Algorithm


Yuxiang Cai, Lijun Cai, Ting Fu,Yong Ye, Sheng Zhou

Corresponding Author:
Yuxiang Cai

State Grid Fujian Electric Power Co., Ltd. information communication branch,Fuzhou,Fujian 350003,China


With the rapid development of the Internet and the Internet of Things, the degree of informatization of various industries has rapidly increased, and modern computers can easily collect large amounts of data. Therefore, various industries have begun to adopt large-scale databases to collect more. The information, and through this information to get more knowledge, resulting in a huge amount of data. In this paper, the clustering algorithm is used to analyze massive unstructured data, and the parameters such as the number of accesses and the duration of use are considered, so that the unstructured data can improve the accuracy when searching, so as to more effectively meet the needs of users.


Clustering algorithm; Unstructured data; Data clustering; Clustering evaluation; Data mining

Cite This Paper

Yuxiang Cai, Lijun Cai, Ting Fu,Yong Ye, Sheng Zhou. Analysis of Massive Unstructured Data Model Based on Clustering Algorithm. Academic Journal of Computing & Information Science (2018) Vol. 1: 28-35.


[1] Yin, C., Zhang, S., Xi, J., & Wang, J. (2016). An improved anonymity model for big data security based on clustering algorithm. Concurrency & Computation Practice & Experience, Vol. 29, No.7, pp.11-19.
[2] Liu, Sanya Ni, Cheng Liu, Zhi Peng, Xian Cheng, Hercy N. H. (2017). Mining individual learning topics in course reviews based on author topic model. International Journal of Distance Education Technologies, Vol. 15, No.3, pp.118-126.
[3] GJ Edgar, AE Bates, TJ Bird, AH Jones, S Kininmonth, & RD StuartSmith, et al. (2016). New approaches to marine conservation through the scaling up of ecological data. Ann Rev Mar Sci, Vol. 8, No.1, pp.435-461.
[4] Quantin, M., Hervy, B., Laroche, F., & Bernard, A. (2016). Supervised process of un-structured data analysis for knowledge chaining . Procedia Cirp, No. 50, 436-441.
[5] Leng, J., & Jiang, P. (2017). Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Transactions on Systems Man & Cybernetics Systems, No. 99, pp.11-13.