TY - JOUR
T1 - Mining DAG patterns from DAG databases
AU - Chen, Yen Liang
AU - Kao, Hung Pin
AU - Ko, Ming Tat
PY - 2004
Y1 - 2004
N2 - Data mining extracts implicit, previously unknown and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding frequent patterns in databases. Although much work has been done to this problem, to the best of our knowledge, no previous research studies how to find frequent DAG (directed acyclic graph) patterns from DAG data. Without such a mining method, the knowledge cannot be discovered from the databases storing DAG data such as family genealogy profiles, product structures, XML documents and course structures. Therefore, a solution method containing four stages is proposed in this paper to discover frequent DAG patterns from DAG databases.
AB - Data mining extracts implicit, previously unknown and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding frequent patterns in databases. Although much work has been done to this problem, to the best of our knowledge, no previous research studies how to find frequent DAG (directed acyclic graph) patterns from DAG data. Without such a mining method, the knowledge cannot be discovered from the databases storing DAG data such as family genealogy profiles, product structures, XML documents and course structures. Therefore, a solution method containing four stages is proposed in this paper to discover frequent DAG patterns from DAG databases.
UR - http://www.scopus.com/inward/record.url?scp=35048887603&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-27772-9_58
DO - 10.1007/978-3-540-27772-9_58
M3 - 期刊論文
AN - SCOPUS:35048887603
SN - 0302-9743
VL - 3129
SP - 579
EP - 588
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -