Abstract
A novel clustering method based on spectral clustering theory and spectral cut standard is proposed via analyzing the characteristics of short text and the defects of the existing clustering algorithms. First of all, a weighted undirected graph is created according to spectral clustering theory, similarity between node and node is calculated on graph, and a symmetrical documents similarity matrix is constructed, which provides all information for the clustering algorithm. Inspired by Greedy strategy, we utilize prim to develop PrimMAE algorithm for the purpose of partitioning graph into two parts, in which RMcut is termination condition of partitioning process, and then it is fed into CASC algorithm to cut the documents set iteratively. Ultimately, high quality clustering results demonstrate the effectiveness of the new clustering algorithm.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China under Grant No. 61272088, the Natural Science Foundation for Young Scientists of Gansu Province, China (Grant No. 1308TJY085, 145RJYA259), Youth Teacher Scientific Capability Promoting Project of Northwest Normal University (No. NWNU-LKQN-13-23).
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Li, X., He, T., Ran, H., Lu, X. (2016). A Novel Graph Partitioning Criterion Based Short Text Clustering Method. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_32
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DOI: https://doi.org/10.1007/978-3-319-42297-8_32
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