Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Scoring the Data Using Association Rules
Applied Intelligence
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Efficient calendar based temporal association rule
ACM SIGMOD Record
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
Data Mining and Knowledge Discovery
Mining maximal hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
BitTableFI: An efficient mining frequent itemsets algorithm
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
A new approach to mine frequent patterns using item-transformation methods
Information Systems
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Realistic Synthetic Data for Testing Association Rule Mining Algorithms for Market Basket Databases
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Updating generalized association rules with evolving taxonomies
Applied Intelligence
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Handling Dynamic Weights in Weighted Frequent Pattern Mining
IEICE - Transactions on Information and Systems
DRFP-tree: disk-resident frequent pattern tree
Applied Intelligence
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
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
A tree-based approach for mining frequent weighted utility itemsets
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Stream mining on univariate uncertain data
Applied Intelligence
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
Frequent episode mining within the latest time windows over event streams
Applied Intelligence
Mining non-redundant time-gap sequential patterns
Applied Intelligence
| Hi-index | 0.00 |
Traditional frequent pattern mining methods consider an equal profit/weight for all items and only binary occurrences (0/1) of the items in transactions. High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. However, most of the existing high utility pattern mining algorithms suffer in the level-wise candidate generation-and-test problem and generate too many candidate patterns. Moreover, they need several database scans which are directly dependent on the maximum candidate length. In this paper, we present a novel tree-based candidate pruning technique, called HUC-Prune (High Utility Candidates Prune), to solve these problems. Our technique uses a novel tree structure, called HUC-tree (High Utility Candidates tree), to capture important utility information of the candidate patterns. HUC-Prune avoids the level-wise candidate generation process by adopting a pattern growth approach. In contrast to the existing algorithms, its number of database scans is completely independent of the maximum candidate length. Extensive experimental results show that our algorithm is very efficient for high utility pattern mining and it outperforms the existing algorithms.