On efficient and effective association rule mining from XML data

摘要

[Abstract]: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently and effectively. In XAR-Miner, raw XML data are first transformed to either an Indexed Content Tree (IX-tree) or Multirelational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection in the AR mining.udConcepts that are relevant to the AR mining task are generalized to produce generalized meta-patterns. A suitable metric is devised for measuring the degreeudof concept generalization in order to prevent under-generalization or overgeneralization. Resultant generalized meta-patterns are used to generate large ARsudthat meet the support and confidence levels. An efficient AR mining algorithm is also presented based on candidate AR generation in the hierarchy of generalizedudmeta-patterns. The experiments show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in orderudto perform each of the mining tasks.