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
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
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 survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
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
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
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
Adaptive learning and mining for data streams and frequent patterns
ACM SIGKDD Explorations Newsletter
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Interactive visual exploration of neighbor-based patterns in data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Incremental mining of closed frequent subtrees
DS'10 Proceedings of the 13th international conference on Discovery science
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
CLUES: a unified framework supporting interactive exploration of density-based clusters in streams
Proceedings of the 20th ACM international conference on Information and knowledge management
Kernel-based selective ensemble learning for streams of trees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A lossy counting based approach for learning on streams of graphs on a budget
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mining of closed frequent subtrees from frequently updated databases
Intelligent Data Analysis
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Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees adaptively from data streams that change over time. Our approach is based on an efficient representation of trees and a low complexity notion of relaxed closed trees, and leads to an on-line strategy and an adaptive sliding window technique for dealing with changes over time. More precisely, we first present a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop three closed tree mining algorithms: an incremental one IncTreeNat, a sliding-window based one, WinTreeNat, and finally one that mines closed trees adaptively from data streams, AdaTreeNat. To the best of our knowledge this is the first work on mining frequent closed trees in streaming data varying with time. We give a first experimental evaluation of the proposed algorithms.