Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Interactive sequence discovery by incremental mining
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Mining maximal frequent itemsets from data streams
Journal of Information Science
An Effective Algorithm for Mining Weighted Association Rules in Telecommunication Networks
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
Mining the Weighted Frequent XML Query Pattern
IWSCA '08 Proceedings of the 2008 IEEE International Workshop on Semantic Computing and Applications
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
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
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams
IEEE Transactions on Knowledge and Data Engineering
Knowledge and Information Systems
Mining association rules with multi-dimensional constraints
Journal of Systems and Software
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
Efficient Mining of Large Maximal Bicliques from 3D Symmetric Adjacency Matrix
IEEE Transactions on Knowledge and Data Engineering
Weighted approximate sequential pattern mining within tolerance factors
Intelligent Data Analysis
Parallel mining of maximal sequential patterns using multiple samples
The Journal of Supercomputing
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Maximal frequent pattern mining has been suggested for data mining to avoid generating a huge set of frequent patterns. Conversely, weighted frequent pattern mining has been proposed to discover important frequent patterns by considering the weighted support. We propose two mining algorithms of maximal correlated weight frequent pattern MCWP, termed MCWPWA based on Weight Ascending order and MCWPSD based on Support Descending order, to mine a compact and meaningful set of frequent patterns. MCWPSD obtains an advantage in conditional database access, but may not obtain the highest weighted item of the conditional database to mine highly correlated weight frequent patterns. Thus, we suggest a technique that uses additional conditions to prune lowly correlated weight items before the subsets checking process. Analyses show that our algorithms are efficient and scalable.