Mining association rules in tree structured XML data

Autor:	Joon Suk Ryu, Won-Young Kim, Juryon Paik, Ung-Mo Kim, Junghyun Nam
Rok vydání:	2009
Předmět:	Incremental decision tree Association rule learning computer.internet_protocol Computer science media_common.quotation_subject computer.software_genre Tree (data structure) Tree traversal Tree structure Simple (abstract algebra) Simplicity Data mining computer XML media_common
Zdroj:	Int. Conf. Interaction Sciences
Popis:	XML is increasingly popular for knowledge representations. However, mining association rules from them is a challenging issue since XML data is usually poorly supported by the current database systems due to its tree structure. Several encouraging attempts at developing methods for mining rules in tree dataset have been proposed, but simplicity and efficiency still remain significant impediments for further development. What is needed is a clear and simple methodology for finding the rules that are hidden in the heterogeneous tree data. In this paper, we adjust and fine-tune the label projection method which has been recently published to compute association rules from trees. The suggested approach avoids the computationally intractable problem caused by the number of nodes contained in the tree dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::82aaa7760f5365ff3b86f38bf154c8ac https://doi.org/10.1145/1655925.1656072 Zobrazit plný text záznamu