Abstract
Bisecting K-means clustering method belongs to the hierarchical algorithm in text clustering, in which the selection of K value and initial center of mass will affect the final result of clustering. Chinese word segmentation has the characteristics of vague word and word boundary, etc. We transformed the corpus into word vector by word2vec, reduced the dimension of data by ontology modeling, and cleaned the data by jieba word segmentation and TF-IDF to improve the accuracy of the data. We propose an improved algorithm based on hierarchical clustering and Bisecting K-means clustering to cluster the data many times until it converges. Through experiments, it is proved that the clustering result of this method is better than that of K-means clustering algorithm and Bisecting K-means clustering algorithm.
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Acknowledgements
This work was partially supported by NSFC (No. 61807024).
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Zi, Y., Kun, L., Zhang, Z., Wang, C., Peng, Z. (2020). An Improved Bisecting K-Means Text Clustering Method. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2019. Advances in Intelligent Systems and Computing, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-34387-3_19
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DOI: https://doi.org/10.1007/978-3-030-34387-3_19
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