Abstract
Nowadays, document clustering technology has been extensively used in text mining, information retrieval systems and etc. The input of network is the key problem for topical concept utilizing the Neural Network. This paper presents an input model of Neural Network that calculates the Mutual Information between contextual words and ambiguous word by using statistical method and taking the contextual words to certain number beside the topical concept according to (-M, +N). In this paper, we introduce a novel topical document clustering method called Document Characters Indexing Clustering (DCIC), which can identify topics accurately and cluster documents according to these topics. In DCIC, “topic elements” are defined and extracted for indexing base clusters. Additionally, document characters are investigated and exploited. Experimental results show that DCIC based on BP Neural Networks models can gain a higher precision (92.76%) than some widely used traditional clustering methods.
The work is supported by the S&T plan projects of Hubei Provincial Education Department of China (No.Q20122207).
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Fu, X., Ding, Y. (2014). The Research of Document Clustering Topical Concept Based on Neural Networks. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_69
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DOI: https://doi.org/10.1007/978-3-319-12436-0_69
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