Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient mining method for retrieving sequential patterns over online data streams
Journal of Information Science
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Research issues in data stream association rule mining
ACM SIGMOD Record
Online mining of frequent query trees over XML data streams
Proceedings of the 15th international conference on World Wide Web
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
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining sequential patterns from data streams: a centroid approach
Journal of Intelligent Information Systems
Quality-Aware Sampling and Its Applications in Incremental Data Mining
IEEE Transactions on Knowledge and Data Engineering
Mining maximal frequent itemsets from data streams
Journal of Information Science
Answering ad hoc aggregate queries from data streams using prefix aggregate trees
Knowledge and Information Systems
An on-line interactive method for finding association rules data streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Incremental maintenance of generalized association rules under taxonomy evolution
Journal of Information Science
On-line generation association rules over data streams
Information and Software Technology
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
A semi-random multiple decision-tree algorithm for mining data streams
Journal of Computer Science and Technology
Discovering frequent sets from data streams with CPU constraint
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Short communication: TOPSIS: Finding Top-K significant N-itemsets in sliding windows adaptively
Knowledge-Based Systems
Online mining of frequent sets in data streams with error guarantee
Knowledge and Information Systems
DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
GraSeq: A Novel Approximate Mining Approach of Sequential Patterns over Data Stream
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Efficient Approximate Mining of Frequent Patterns over Transactional Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
A coarse-grain grid-based subspace clustering method for online multi-dimensional data streams
Proceedings of the 17th ACM conference on Information and knowledge management
Maintaining frequent closed itemsets over a sliding window
Journal of Intelligent Information Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Pattern discovery and change detection of online music query streams
Multimedia Tools and Applications
Incrementally Mining Recently Repeating Patterns over Data Streams
New Frontiers in Applied Data Mining
Mining frequent closed itemsets from a landmark window over online data streams
Computers & Mathematics with Applications
Frequent items in streaming data: An experimental evaluation of the state-of-the-art
Data & Knowledge Engineering
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Data Mining and Knowledge Discovery
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Mining Frequent Patterns from Network Data Flow
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Approximate Frequent Itemset Discovery from Data Stream
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
A fast approximation strategy for summarizing a set of streaming time series
Proceedings of the 2010 ACM Symposium on Applied Computing
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Approximately mining recently representative patterns on data streams
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
CLAIM: an efficient method for relaxed frequent closed itemsets mining over stream data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Discovery of frequent distributed event patterns in sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Adequacy of data for mining individual friendship pattern from cellular phone call logs
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
A test paradigm for detecting changes in transactional data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Mining frequent patterns in an arbitrary sliding window over data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Mining top-k frequent closed itemsets over data streams using the sliding window model
Expert Systems with Applications: An International Journal
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
i-SEE: integrated stream execution environment over on-line data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A new paradigm of ranking & searching in learning object repository
Proceedings of the second ACM international workshop on Multimedia technologies for distance leaning
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Measures for comparing association rule sets
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Mining informative rule set for prediction over a sliding window
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
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
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Data Mining and Knowledge Discovery
Automatic assignment of item weights for pattern mining on data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams
Journal of Information Science
Discovery of implicit correlation between shared information in an open environment
MTDL '11 Proceedings of the third international ACM workshop on Multimedia technologies for distance learning
Search method of time sensitive frequent itemsets in data streams
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Dynamically mining frequent patterns over online data streams
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
EStream: online mining of frequent sets with precise error guarantee
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Granularity adaptive density estimation and on demand clustering of concept-drifting data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Adaptive load shedding for mining frequent patterns from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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
Mining recent frequent itemsets in data streams by radioactively attenuating strategy
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
User subjectivity in change modeling of streaming itemsets
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Repository and search based on distance learning standards
ICHL'09 Proceedings of the Second international conference on Hybrid Learning and Education
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
Efficient mining of frequent items coupled with weight and /or support over progressive databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
A sliding window-based false-negative approach for ubiquitous data stream analysis
International Journal of Communication Systems
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
Computers & Mathematics with Applications
Recent frequent itemsets mining over data streams
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
An adaptive algorithm for finding frequent sets in landmark windows
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
ACM Transactions on Management Information Systems (TMIS)
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
LONET: An interactive search network for intelligent lecture path generation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Identifying streaming frequent items in ad hoc time windows
Data & Knowledge Engineering
Mining frequent items in data stream using time fading model
Information Sciences: an International Journal
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 unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, specially for an online data stream, can provide valuable information for the analysis of the data stream. In addition, monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge. However, most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present. This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream. The effect of old transactions on the mining result of the data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as main memory usage. Finally, the proposed method is analyzed by a series of experiments.