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
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 hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Mining Closed and Maximal Frequent Induced Free Subtrees
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
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
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
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Efficient Mining of Large Maximal Bicliques from 3D Symmetric Adjacency Matrix
IEEE Transactions on Knowledge and Data Engineering
Weighted approximate sequential pattern mining within tolerance factors
Intelligent Data Analysis
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
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
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Sequential patterns mining and gene sequence visualization to discover novelty from microarray data
Journal of Biomedical Informatics
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
As data have been accumulated more quickly in recent years, corresponding databases have also become huger, and thus, general frequent pattern mining methods have been faced with limitations that do not appropriately respond to the massive data. To overcome this problem, data mining researchers have studied methods which can conduct more efficient and immediate mining tasks by scanning databases only once. Thereafter, the sliding window model, which can perform mining operations focusing on recently accumulated parts over data streams, was proposed, and a variety of mining approaches related to this have been suggested. However, it is hard to mine all of the frequent patterns in the data stream environment since generated patterns are remarkably increased as data streams are continuously extended. Thus, methods for efficiently compressing generated patterns are needed in order to solve that problem. In addition, since not only support conditions but also weight constraints expressing items' importance are one of the important factors in the pattern mining, we need to consider them in mining process. Motivated by these issues, we propose a novel algorithm, weighted maximal frequent pattern mining over data streams based on sliding window model (WMFP-SW) to obtain weighted maximal frequent patterns reflecting recent information over data streams. Performance experiments report that MWFP-SW outperforms previous algorithms in terms of runtime, memory usage, and scalability.