A Text Association Rules Mining Method Based on Concept Algebra

The Concept Algebra (CA) based text representation method which can be auto-constructed and used in ordinary texts' includes more semantic information compared to Keyword methods. This method provides a new way of thinking of more accurately mining meaningful association rules. In this paper concepts association rules mining is based on CA represented texts, and use the relations among concepts to optimize the mining results with definition and calculation of association weight and rule strength. Compared with traditional method regarding keyword as independent to each other lacks semantic information while domain-oriented method would be applicable to specific areas with specialists construction. Method in this paper use CA's superiority on utilizing Ri and Ro of concepts to remedy the two deficiencies effectively which is proved by the theoretical and experiment.

[1]  Feiyue Ye,et al.  Finding the Semantic Relation between Web Pages through Topic Knowledge Repository , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[2]  Xue Chen,et al.  Building Association Link Network for Semantic Link on Web Resources , 2011, IEEE Transactions on Automation Science and Engineering.

[3]  Li Zhenghua.  Language Technology Platform , 2011 .

[4]  Yingxu Wang,et al.  On Concept Algebra: A Denotational Mathematical Structure for Knowledge and Software Modeling , 2008, Int. J. Cogn. Informatics Nat. Intell..

[5]  Jun Zhang,et al.  Text Semantic Mining Model Based on the Algebra of Human Concept Learning , 2011, Int. J. Cogn. Informatics Nat. Intell..

[6]  Paul Buitelaar,et al.  RelExt: A Tool for Relation Extraction from Text in Ontology Extension , 2005, SEMWEB.

[7]  Amedeo Napoli,et al.  Towards a text mining methodology using association rule extraction , 2006, Soft Comput..

[8]  Xiangfeng Luo,et al.  A Text Representation and Retrieval Method Based on Concept Algebra , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.