Abstract
Organizing streaming documents from time-varying dataset is meaningful but difficult because topics evolve over time. Dynamic document clustering is a vital research problem, which helps to group the time-varying documents into a number of clusters corresponding to their underlying topics. Datasets are partitioned into a set of time slides to transfer the streaming document clustering from a continuous problem to a categorical one. Traditional dynamic document clustering approach tends to inherit topic information over time directly with no consideration of the nature of datasets. In this paper, we design a novel prior-adjusted dynamic document clustering approach, namely PADC, which is able to adjust the topic inheritance process according to two important of datasets characteristics, in particular, the interval between dataset time slides and the size of dataset time slides. A collapsed Gibbs sampling algorithm is investigated to infer the document structure for all time slides with underlying time-varying topics. Parameters for underlying topics inheritance, as well as parameters of the number of clusters in each time slide, are estimated simultaneously. Extensive experiments have been conducted comparing the PADC model with state-of-the-art dynamic document clustering approaches. Experimental results demonstrate that the PADC model is robust and effective for the dynamic document clustering problem.
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The description can be found at http://people.csail.mit.edu/jrennie/20Newsgroups.
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Acknowledgments
The work described in this paper is substantially supported by the National Natural Science Foundation of China (Grant No. U1836205), the National Natural Science Foundation of China (Grant No. 61462011), the Major Research Program of the National Natural Science Foundation of China (Grant No. 91746116), the Major Applied Basic Research Program of Guizhou Province (Grant No. JZ20142001), the Major Special Science and Technology Projects of Guizhou Province (Grant No. [2017]3002), and the Science and Technology Projects of Guizhou Province (Grant No. [2018]1035).
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Huang, R. et al. (2019). Adjusting the Inheritance of Topic for Dynamic Document Clustering. In: Sun, X., He, K., Chen, X. (eds) Theoretical Computer Science. NCTCS 2019. Communications in Computer and Information Science, vol 1069. Springer, Singapore. https://doi.org/10.1007/978-981-15-0105-0_4
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