Abstract
In order to find key and useful messages among massive online resources, this paper propose a method to classify documents about soybean metabolism based on Singular Value Decomposition (SVD) and Fuzzy c-Means(FCM). Singular Value Decomposition (SVD) is an important way of matrix decomposition, which can represent a complex matrix by dividing it into smaller and simpler submatrices that describe important properties of matrices. After the dimension reduction, the Fuzzy c-Means (FCM) is used for clustering, which makes the objects divided into the same cluster have the highest similarity, while the object between different clusters have the lowest similarity. Besides, term frequency (TF) and entropy weight method (EWM) can also be used to construct matrix.
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Acknowledge
This work was supported by grants from the Fundamental Research Funds for the Key Research Programm of Chongqing Science & Technology Commission (grant no. cstc2017rgzn-zdyf0064), the Chongqing Provincial Human Resource and Social Security Department (grant no. cx2017092), the Central Universities in China (grant nos. 2018CDXYRJ0030, CQU0225001104447). The authors would like to express their gratitude to all the subjects that participated in the experiments. This study is supported by Science and Technology Innovation Project of Foshan City, China (Grant No. 2015IT100095), the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2016-br03), CERNET Innovation Project (Grant No. NGII20150603) and Science and Technology Planning Project of Guangdong Province, China (Grant No. 2016B010108002).
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Yang, N., Li, S., Sun, R., Yang, Y. (2018). Text Classification Methods Based on SVD and FCM. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_11
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DOI: https://doi.org/10.1007/978-3-030-01298-4_11
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