Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient cluster and decomposition algorithm for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient Mining of Constrained Frequent Patterns from Streams
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Mining spatial association rules in image databases
Information Sciences: an International Journal
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
FIUT: A new method for mining frequent itemsets
Information Sciences: 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
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An approximate approach for mining recently frequent itemsets from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
An efficient algorithm for frequent itemset mining on data streams
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Finding top-k elements in data streams
Information Sciences: an International Journal
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
An improved association rules mining method
Expert Systems with Applications: An International Journal
A time-varying propagation model of hot topic on BBS sites and Blog networks
Information Sciences: an International Journal
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Function and service pattern analysis for facilitating the reconfiguration of collaboration systems
Computers and Industrial Engineering
Mining frequent patterns from dynamic data streams with data load management
Journal of Systems and Software
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Information Sciences: an International Journal
Rare pattern mining on data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
A sliding window based algorithm for frequent closed itemset mining over data streams
Journal of Systems and Software
Mining associated sensor patterns for data stream of wireless sensor networks
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
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
Efficient frequent itemset mining methods over time-sensitive streams
Knowledge-Based Systems
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
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Finding frequent patterns in a continuous stream of transactions is critical for many applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Even though numerous frequent pattern mining algorithms have been developed over the past decade, new solutions for handling stream data are still required due to the continuous, unbounded, and ordered sequence of data elements generated at a rapid rate in a data stream. Therefore, extracting frequent patterns from more recent data can enhance the analysis of stream data. In this paper, we propose an efficient technique to discover the complete set of recent frequent patterns from a high-speed data stream over a sliding window. We develop a Compact Pattern Stream tree (CPS-tree) to capture the recent stream data content and efficiently remove the obsolete, old stream data content. We also introduce the concept of dynamic tree restructuring in our CPS-tree to produce a highly compact frequency-descending tree structure at runtime. The complete set of recent frequent patterns is obtained from the CPS-tree of the current window using an FP-growth mining technique. Extensive experimental analyses show that our CPS-tree is highly efficient in terms of memory and time complexity when finding recent frequent patterns from a high-speed data stream.