Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 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
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining closed relational graphs with connectivity constraints
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Non-Almost-Derivable Frequent Itemsets Mining
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Closed Constrained Gradient Mining in Retail Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering interesting patterns through user's interactive feedback
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Weighted graphs and disconnected components: patterns and a generator
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the Weighted Frequent XML Query Pattern
IWSCA '08 Proceedings of the 2008 IEEE International Workshop on Semantic Computing and Applications
Efficient Discovery of Frequent Approximate Sequential Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
gApprox: Mining Frequent Approximate Patterns from a Massive Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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
Classification rule discovery for the aviation incidents resulted in fatality
Knowledge-Based Systems
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Finding association rules in semantic web data
Knowledge-Based Systems
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
Mining interestingness measures for string pattern mining
Knowledge-Based Systems
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
A novel classification algorithm to noise data
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Discovering forward sequences from temporal data
Knowledge-Based Systems
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Parallel motif extraction from very long sequences
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
| Hi-index | 0.00 |
In data mining area, weighted frequent pattern mining has been suggested to find important frequent patterns by considering the weights of patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, and weighted sub-trees or sub structures. In previous approaches of weighted frequent pattern mining, weighted supports of patterns were exactly matched to prune weighted infrequent patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted frequent patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining approximate weighted frequent patterns. Based on the framework of weight based pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of patterns and a minimum threshold. After that, we address the concept of mining approximate weighted frequent patterns to find important patterns with/without the noisy data. We analyze characteristics of approximate weighted frequent patterns and run extensive performance tests.