Automatic construction of classification dimensions by clustering texts based on common words

Author:

Ma Bing,Zhuge HaiORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference37 articles.

1. Aggarwal, C. C., & Zhai, C. (2012). A survey of text clustering algorithms. Chapter 4 in Mining Text Data (pp. 77-128). Springer. https://link.springer.com/chapter/10.1007/978-1-4614-3223-4_4.

2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Paper presented at the 20th international conference on very large data bases (Pp. 487-499), Santiago, Chile. https://dl.acm.org/doi/10.5555/645920.672836.

3. Beil, F., Ester, M., & Xu, X. (2002). Frequent term-based text clustering. In Paper presented at the 8th ACM SIGKDD international conference on knowledge discovery and data mining (Pp. 436-442), Edmonton, Canada. https://doi.org/10.1145/775047.775110.

4. Probabilistic topic models;Blei;Communications of the ACM,2012

5. Latent dirichlet allocation;Blei;Journal of Machine Learning Research,2003

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