Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
Two-phase algorithms for a novel utility-frequent mining model
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Efficient mining of high utility itemsets from large datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective tree structure for mining high utility itemsets
Expert Systems with Applications: An International Journal
Effective utility mining with the measure of average utility
Expert Systems with Applications: An International Journal
Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis
Data Mining and Knowledge Discovery
Mining high utility mobile sequential patterns in mobile commerce environments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on 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
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
Direct Discovery of High Utility Itemsets without Candidate Generation
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
IEEE Transactions on Knowledge and Data Engineering
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining frequent itemsets in a stream
Information Systems
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
High utility itemset mining considers the importance of items such as profit and item quantities in transactions. Recently, mining high utility itemsets has emerged as one of the most significant research issues due to a huge range of real world applications such as retail market data analysis and stock market prediction. Although many relevant algorithms have been proposed in recent years, they incur the problem of generating a large number of candidate itemsets, which degrade mining performance. In this paper, we propose an algorithm named MU-Growth (Maximum Utility Growth) with two techniques for pruning candidates effectively in mining process. Moreover, we suggest a tree structure, named MIQ-Tree (Maximum Item Quantity Tree), which captures database information with a single-pass. The proposed data structure is restructured for reducing overestimated utilities. Performance evaluation shows that MU-Growth not only decreases the number of candidates but also outperforms state-of-the-art tree-based algorithms with overestimated methods in terms of runtime with a similar memory usage.