Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
DSM-PLW: single-pass mining of path traversal patterns over streaming web click-sequences
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
MARGIN: Maximal Frequent Subgraph Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining maximal hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Mining maximal frequent itemsets from data streams
Journal of Information Science
A Novel Algorithm of Mining Maximal Frequent Pattern Based on Projection Sum Tree
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences
Expert Systems with Applications: An International Journal
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Efficient Mining of Weighted Frequent Patterns over Data Streams
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Knowledge and Information Systems
Statistical Analysis and Data Mining
Expert Systems with Applications: An International Journal
Network traffic monitoring based on mining frequent patterns
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Continuous Subgraph Pattern Search over Certain and Uncertain Graph Streams
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Large Maximal Bicliques from 3D Symmetric Adjacency Matrix
IEEE Transactions on Knowledge and Data Engineering
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining sequential patterns from probabilistic databases
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Parallel mining of maximal sequential patterns using multiple samples
The Journal of Supercomputing
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Sequential patterns mining and gene sequence visualization to discover novelty from microarray data
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Frequent pattern mining over data streams is currently one of the most interesting fields in data mining. Current databases have needed more immediate processes since enormous amounts of data are being accumulated and updated in real time. However, existing traditional approaches have not been entirely suitable for a data stream environment since they operate with more than two database scans. Moreover, frequent pattern mining over data streams mostly generates an enormous number of frequent patterns, thereby causing a significant amount of overheads. In addition, as weight conditions are very useful factors in reflecting importance for each object in the real world, it is necessary to apply them to the mining process in order to obtain more practical, meaningful patterns. To consider and solve these problems, we propose a novel method for mining Weighted Maximal Frequent Patterns (WMFPs) over data streams, called MWS (Maximal frequent pattern mining with Weight conditions over data Streams). MWS guarantees efficient mining performance in the data stream environment by scanning stream databases only once, and prevents overheads of pattern extractions with an abbreviated notation: a maximal frequent pattern form instead of the general one. Furthermore, MWS contributes to enhanced reliability of the mining results by applying weight conditions to each element of the data streams. Extensive experiments report that MWS has outstanding performance in comparison to previous algorithms.