Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Database Mining on Derived Attributes
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Raising data for improved support in rule mining: How to raise and how far to raise
Intelligent Data Analysis
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Pushing Frequency Constraint to Utility Mining Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Weighted Association Rule Mining from Binary and Fuzzy Data
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework
New Frontiers in Applied Data Mining
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
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Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity. However, in real applications, the user may have more interest in the rules that contain those fashionable items that occur frequently. Usually too many outdated items exist in databases, but they seldom occur recently. Those outdated items hamper us to find the interesting rules efficiently and effectively. Another case is the user sometimes may want to mine the association rules with more emphasis on some items. To solve these problems, in this paper, we propose the vertical and mixed weighted association rules. We can divide the database into several time intervals, and assign a weight for each interval. Furthermore, we also assign a weight for each item to identify the important items. We present an algorithm MWAR (Mixed Weighted Association Rules) to handle the problem of mining mixed weighted association rules. The experiments show that the rules from our methods have much better predictive ability on future data. We also demonstrate the efficiency of our methods on real data and synthetic datasets.