Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th International Conference on Data Engineering
On Mining General Temporal Association Rules in a Publication Database
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining General Temporal Association Rules for Items with Different Exhibition Periods
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Temporal Association Rules in Mining Method
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
Discovery of Association Rules in Temporal Databases
ITNG '07 Proceedings of the International Conference on Information Technology
Twain: Two-end association miner with precise frequent exhibition periods
ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental mining for temporal association rules for crime pattern discoveries
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
An efficient technique for incremental updating of association rules
International Journal of Hybrid Intelligent Systems
Hardware-Enhanced Association Rule Mining with Hashing and Pipelining
IEEE Transactions on Knowledge and Data Engineering
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
Future direction of incremental association rules mining
Proceedings of the 47th Annual Southeast Regional Conference
Incremental Updating Algorithm Based on Partial Support Tree for Mining Association Rules
CASE '09 Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Towards the effective temporal association mining of spam blacklists
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
An improved association rules mining method
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique
International Journal of Information Retrieval Research
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This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rules need to be updated. Re-running the temporal mining algorithm every time is ineffective since it neglects the previously discovered rules, and repeats the work done previously. Furthermore, existing incremental mining techniques cannot deal with temporal association rules. In this paper, an incremental algorithm to maintain the temporal association rules in a transaction database is proposed. The algorithm benefits from the results of earlier mining to derive the final mining output. The experimental results on both the synthetic and the real dataset illustrate a significant improvement over the conventional approach of mining the entire updated database.