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Title: Scalable Association Rule Mining with Predicates on Semantic Representations of Data

Finding semantic associations from a vast amount of heterogeneous data is an important and useful task in various applications. We present a framework to extract semantic association patterns directly from a very large graph dataset without the extra step of converting graph data into transaction data.
Authors:
 [1] ;  [1] ;  [1]
  1. ORNL
Publication Date:
OSTI Identifier:
1236594
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: Technology and Applications of Artificial Intelligence, Tainan, Taiwan, 20151120, 20150822
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
ORNL LDRD Director's R&D
Country of Publication:
United States
Language:
English