The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Algorithms on Trees and Graphs
Algorithms on Trees and Graphs
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Unordered Tree Mining with Applications to Phylogeny
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The Art of Computer Programming, Volume 4, Fascicle 4: Generating All Trees--History of Combinatorial Generation (Art of Computer Programming)
Online mining of frequent query trees over XML data streams
Proceedings of the 15th international conference on World Wide Web
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient mining of frequent closed XML query pattern
Journal of Computer Science and Technology
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Adaptive XML Tree Classification on Evolving Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Mining frequent closed rooted trees
Machine Learning
CLAIM: an efficient method for relaxed frequent closed itemsets mining over stream data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Online techniques for dealing with concept drift in process mining
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Mining most frequently changing component in evolving graphs
World Wide Web
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We propose new algorithms for adaptively mining closed rooted trees, both labeled and unlabeled, from data streams that change over time. Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. Our approach is based on an advantageous representation of trees and a low-complexity notion of relaxed closed trees, as well as ideas from Galois Lattice Theory. More precisely, we present three closed tree mining algorithms in sequence: an incremental one, IncTreeMiner, a sliding-window based one, WinTreeMiner, and finally one that mines closed trees adaptively from data streams, AdaTreeMiner. By adaptive we mean here that it presents at all times the closed trees that are frequent in the current state of the data stream. To the best of our knowledge this is the first work on mining closed frequent trees in streaming data varying with time. We give a first experimental evaluation of the proposed algorithms.