Discovering interesting information in XML data with association rules

measures, performance measures Data mining algorithms are designed to extract interesting information from large amounts of data. They usually assume that source data are in relational (tabular) form. However, the recent success of XML as a standard to represent semi-structured data and the increasing amount of data available in XML pose new challenges to the data mining community. In this paper we introduce association rules for XML data. To accomplish this, we propose a new operator, based on XPath and inspired by the syntax of XQuery, which allows us to express complex mining tasks, compactly and intuitively. The operator can indifferently (and simultaneously) target both the content and the structure of the data, since the distinction in XML is slight. 1

Quality and interestingness of association rules derived from data mining of relational and semi-structured data

Deriving useful and interesting rules from a data mining system are essential and important tasks. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. As the data mining techniques are data-driven, it is beneficial to affirm the rules using a statistical approach. It is important to establish the ways in which the existing statistical measures and constraint parameters can be effectively utilized and the sequence of their usage.In this thesis, a systematic way to evaluate the association rules discovered from frequent, closed and maximal itemset mining algorithms; and frequent subtree mining algorithm including the rules based on induced, embedded and disconnected subtrees is presented. With reference to the frequent subtree mining, in addition a new direction is explored based on utilizing the DSM approach capable of preserving all information from tree-structured database in a flat data format, consequently enabling the direct application of a wider range of data mining analysis/techniques to tree-structured data. Implications of this approach were investigated and it was found that basing rules on disconnected subtrees, can be useful in terms of increasing the accuracy and the coverage rate of the rule set.A strategy that combines data mining and statistical measurement techniques such as sampling, redundancy and contradictive checks, correlation and regression analysis to evaluate the rules is developed. This framework is then applied to real-world datasets that represent diverse characteristics of data/items. Empirical results show that with a proper combination of data mining and statistical analysis, the proposed framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy rules. Moreover, the results reveal the important characteristics and differences between mining frequent, closed or maximal itemsets; and mining frequent subtree including the rules based on induced, embedded and disconnected subtrees; as well as the impact of confidence measure for the prediction and classification task