TY - GEN
T1 - XAR-Miner
T2 - 14th International World Wide Web Conference, WWW2005
AU - Zhang, Sheng
AU - Zhang, Ji
AU - Liu, Han
AU - Wang, Wei
PY - 2005
Y1 - 2005
N2 - In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.
AB - In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.
KW - Association rule mining
KW - Meta-patterns
KW - XML data
UR - http://www.scopus.com/inward/record.url?scp=35148826115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35148826115&partnerID=8YFLogxK
U2 - 10.1145/1062745.1062785
DO - 10.1145/1062745.1062785
M3 - Conference contribution
AN - SCOPUS:35148826115
SN - 1595930515
SN - 9781595930514
T3 - 14th International World Wide Web Conference, WWW2005
SP - 894
EP - 895
BT - 14th International World Wide Web Conference, WWW2005
Y2 - 10 May 2005 through 14 May 2005
ER -