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Measuring the semantic discrimination capability of association relations
Author(s) -
Xu Zheng,
Luo Xiangfeng,
Mei Lin,
Hu Chuanping
Publication year - 2014
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2999
Subject(s) - computer science , cluster analysis , association (psychology) , relation (database) , semantics (computer science) , data mining , clustering coefficient , binary number , class (philosophy) , task (project management) , measure (data warehouse) , information retrieval , artificial intelligence , mathematics , engineering , philosophy , epistemology , arithmetic , systems engineering , programming language
SUMMARY Association relations between concepts are a class of simple but powerful regularities in binary data, which play important roles in enterprises and organizations with huge amounts of data. However, although there can be easily large number of association relation mined from databases, since existing objective and subjective methods scarcely take semantics into consideration, it has been recognized early in the knowledge discovery literature that most of them are of no interest to the user. In this paper, the semantic discrimination capability (SDC) of association relation is measured based on discrimination value model first. The formula of SDC integrating both statistical and graph features is proposed from five different strategies. The high correlation coefficient of the proposed method against discrimination value shows that the proposed SDC measure is accuracy. Moreover, an application using SDC on document clustering is carried out, which shows that SDC has broad prospects on data‐related task such as document clustering. Copyright 2013 John Wiley © Sons, Ltd.