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Data Analysis and Knowledge Discovery  2016, Vol. 32 Issue (12): 50-56    DOI: 10.11925/infotech.1003-3513.2016.12.07
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Mining Document Topics Based on Association Rules
Guangce Ruan(),Lei Xia
Department of Information Management, East China Normal University, Shanghai 200241, China
Shanghai Library, Shanghai 200031, China
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[Objective]This study is to accurately identify potential knowledge correlations among textual information, and then enrich the methodology of knowledge mining. [Methods] First, we combined the topic model and association rules. Second, used the LDA model to extract topic set from the texts, which not only reduced the textual dimension but also realized the semantic space expression. Finally, we analyzed the semantic ties among the topics with association rules. [Results] We effectively found the potential knowledge association from the document texts with reasonable degrees of support and confidence, and then improved model’s “understanding” of the textual message. [Limitations] While preprocessing data, the self-defined dictionary posed some negative effects to the results. [Conclusions] The proposed method could extract the latent semantic association from unstructured textual information, and then improve the performance of knowledge discovery systems.

Key wordsAssociation rules      Topic model      Text topics     
Received: 07 September 2016      Published: 22 January 2017

Cite this article:

Guangce Ruan, Lei Xia. Mining Document Topics Based on Association Rules. Data Analysis and Knowledge Discovery, 2016, 32(12): 50-56.

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