Mining frequent patterns from XML data: Efficient algorithms and design trade-offs

Authors:
Aıda Jiménez;Fernando Berzal;Juan-Carlos Cubero
Affiliations:
Dept. Computer Science and Artificial Intelligence, ETSIIT - University of Granada, 18071 Granada, Spain;Dept. Computer Science and Artificial Intelligence, ETSIIT - University of Granada, 18071 Granada, Spain;Dept. Computer Science and Artificial Intelligence, ETSIIT - University of Granada, 18071 Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

XML documents are now ubiquitous and their current applications are countless, from representing semi-structured documents to being the de facto standard for exchanging information. Viewed as partially-ordered trees, XML documents are amenable to efficient data mining techniques. In this paper, we describe how scalable algorithms can be used to mine frequent patterns from partially-ordered trees and discuss the trade-offs that are involved in the design of such algorithms.