Latent association rule cluster based model to extract topics for classification and recommendation applications
作者：
Highlights：
• A topic modeling approach that explores clustering of association rules.
• The approach provides topics with correlation of terms and low dimensionality.
• The topics present better interpretability than the state-of-the-art approaches.
• The topics are used in text classification and page recommendation applications.
• The topics provided by our approach can be used to improve both applications.
摘要
•A topic modeling approach that explores clustering of association rules.•The approach provides topics with correlation of terms and low dimensionality.•The topics present better interpretability than the state-of-the-art approaches.•The topics are used in text classification and page recommendation applications.•The topics provided by our approach can be used to improve both applications.
论文关键词：Document representation,Topic model,Association rules,Clustering,Text classification,Context-aware recommender systems
论文评审过程：Received 19 August 2017, Revised 7 June 2018, Accepted 8 June 2018, Available online 15 June 2018, Version of Record 22 June 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.021
