Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Mining Weighted Frequent Patterns Using Adaptive Weights
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Efficient Single-Pass Mining of Weighted Interesting Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Mining Weighted Frequent Patterns in Incremental Databases
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Handling Dynamic Weights in Weighted Frequent Pattern Mining
IEICE - Transactions on Information and Systems
On pushing weight constraints deeply into frequent itemset mining
Intelligent Data Analysis
Association rules mining with relative weighted support
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Expert Systems with Applications: An International Journal
Frequent pattern mining with preferences–utility functions approach
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Considering re-occurring features in associative classifiers
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule; we call them weighted association rules (WAR). One example of such a rule might be 80% of people buying more than three bottles of soda will also be likely to buy more than four packages of snack food, while a conventional association rule might just be 60% of people buying soda will be also be likely to buy snack food. Thus WARs cannot only improve the confidence of the rules, but also provide a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by first ignoring the weight and finding the frequent itemsets (via a traditional frequent itemset discovery algorithm), followed by introducing the weight during the rule generation. Specifically, the rule generation is achieved by partitioning the weight domain space of each frequent itemset into fine grids, and then identifying the popular regions within the domain space to derive WARs. This approach does not only support the batch mode mining, i.e., finding WARs for the dataset, but also supports the interactive mode, i.e., finding and refining WARs for a given (set) of frequent itemset(s).