Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Clustering over Multiple Evolving Streams by Events and Correlations
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Information discovery across multiple streams
Information Sciences: an International Journal
SKIF: a data imputation framework for concept drifting data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Group detection and relation analysis research for web social network
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Adaptive stratified reservoir sampling over heterogeneous data streams
Information Systems
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Mining frequent patterns from data streams has drawn increasing attention in recent years. However, previous mining algorithms were all focused on a single data stream. In many emerging applications, it is of critical importance to combine multiple data streams for analysis. For example, in real-time news topic analysis, it is necessary to combine multiple news report streams from dierent media sources to discover collaborative frequent patterns which are reported frequently in all media, and comparative frequent patterns which are reported more frequently in a media than others. To address this problem, we propose a novel frequent pattern mining algorithm Hybrid-Streaming, H-Stream for short. H-Stream builds a new Hybrid-Frequent tree to maintain historical frequent and potential frequent itemsets from all data streams, and incrementally updates these itemsets for efficient collaborative and comparative pattern mining. Theoretical and empirical studies demonstrate the utility of the proposed method.