Meaningful change detection in structured data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining Association Rules from XML Data
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient and Scalable Algorithm for Clustering XML Documents by Structure
IEEE Transactions on Knowledge and Data Engineering
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering frequently changing structures from historical structural deltas of unordered XML
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data & Knowledge Engineering - Special issue: WIDM 2004
Mirror site maintenance based on evolution associations of web directories
Proceedings of the 16th international conference on World Wide Web
Bloom histogram: path selectivity estimation for XML data with updates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Mining trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. These efforts largely assumed that the trees are static. However, in many real applications, tree data are evolutionary in nature. In this paper, we focus on mining evolution patterns from historical tree-structured data. Specifically, we propose a novel approach to discover negatively correlated subtree patterns (nectar s) from a sequence of historical versions of unordered trees.The objective is to extract subtrees that are negatively correlated in undergoing structural changes. We propose an algorithm called nectar -Miner based on a set of evolution metrics to extract nectar s. nectar s can be useful in several applications such as maintaining mirrors of a website and maintaining xml path selectivity estimation. Extensive experiments show that the proposed algorithm has good performance and can discover nectar s accurately.