Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh 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
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Association Rules: Anti-Skew Algorithms
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
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 Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and 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
Discovering Temporal Association Rules: Algorithms, Language and System
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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The discovery of association relationship among the data in a huge database has been known to be useful in selective marketing, decision analysis, and business management. A significant amount of research effort has been elaborated upon the development of efficient algorithms for data mining. However, without fully considering the time-variant characteristics of items and transactions, it is noted that some discovered rules may be expired from users' interest. In other words, some discovered knowledge may be obsolete and of little use, especially when we perform the mining schemes on a transaction database of short life cycle products. This aspect is, however, rarely addressed in prior studies. To remedy this, we broaden in this paper the horizon of frequent pattern mining by introducing a weighted model of transaction-weighted association rules in a time-variant database. Specifically, we propose an efficient Progressive Weighted Miner (abbreviatedly as PWM) algorithm to perform the mining for this problem as well as conduct the corresponding performance studies. In algorithm PWM, the importance of each transaction period is first reflected by a proper weight assigned by the user. Then, PWM partitions the time-variant database in light of weighted periods of transactions and performs weighted mining. Algorithm PWM is designed to progressively accumulate the itemset counts based on the intrinsic partitioning characteristics and employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. With this design, algorithm PWM is able to efficiently produce weighted association rules for applications where different time periods are assigned with different weights and lead to results of more interest.