Online mining of frequent query trees over XML data streams
Proceedings of the 15th international conference on World Wide Web
Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Phrase Recommendation Algorithm Based on Query Stream Mining in Web Search Engines
Algorithms and Models for the Web-Graph
RETRACTED: Efficient mining of temporal emerging itemsets from data streams
Expert Systems with Applications: An International Journal
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Incrementally Mining Recently Repeating Patterns over Data Streams
New Frontiers in Applied Data Mining
WSFI-Mine: Mining Frequent Patterns in Data Streams
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Online mining of temporal maximal utility itemsets from data streams
Proceedings of the 2010 ACM Symposium on Applied Computing
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Mining based decision support multi-agent system for personalized e-healthcare service
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
MFISW: a new method for mining frequent itemsets in time and transaction sensitive sliding window
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Positive and negative association rule mining on XML data streams in database as a service concept
Expert Systems with Applications: An International Journal
A false negative maximal frequent itemset mining algorithm over stream
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Extrapolation prefix tree for data stream mining using a landmark model
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Stream mining on univariate uncertain data
Applied Intelligence
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
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A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.