Correlated Utility-based Pattern Mining

  title={Correlated Utility-based Pattern Mining},
  author={Wensheng Gan and Chun-Wei Lin and H. C. Chao and Hamido Fujita and Philip S. Yu},
  journal={Inf. Sci.},

Figures and Tables from this paper

A Survey of Correlated High Utility Pattern Mining
A detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties is presented.
Mining with Rarity for Web Intelligence
This paper addresses the problem of mining with rarity and proposes an efficient algorithm, named HURI-Miner, which uses the data structure called revised utility-list to find HURIs from a transaction database, and utilizes several powerful pruning strategies to prune the search space and save the computational complexity.
Frequent high minimum average utility sequence mining with constraints in dynamic databases using efficient pruning strategies
To efficiently find all FHAUSs with constraints, some novel upper bounds and weak upper bounds on the average-utility, which satisfy downward-closure (DC) properties or DC-like properties are proposed and integrated into an algorithm named C-FHAUSPM (Constrained Frequent High minimum Average-Utility Sequential Pattern Mining), which is highly efficient in terms of runtime and memory usage.
An efficient method for mining multi-level high utility Itemsets
To accurately find multi-level HUIs from transaction databases enhanced with taxonomy information, a new algorithm called MLHMiner (Multiple-Level HMiner) is proposed, which is an extended version of the HMiner algorithm that is capable of identifying useful patterns from different abstraction levels with high efficiency.


Mining high utility itemsets without candidate generation
This paper proposes an algorithm, called HUI-Miner (High Utility Itemset Miner), which can efficiently mine high utility itemsets from the utility-lists constructed from a mined database and compares it with the state-of-the-art algorithms on various databases.
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
This paper presents a Two-Phase algorithm to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets on synthetic and real databases.
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
This paper proposes three novel tree structures to efficiently perform incremental and interactive HUP mining that can capture the incremental data without any restructuring operation, and shows that these tree structures are very efficient and scalable.
Extracting Non-redundant Correlated Purchase Behaviors by Utility Measure
An extensive experimental study showed that the novel non-redundant correlated high-utility pattern has more effectiveness than the previous knowledge representation and the proposed algorithm is efficient in terms of execution time and memory usage.
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.
Principles of Economics
ECONOMICS admit of being reduced to principles more than other sciences dealing with human actions, for the reason which Prof. Marshall has thus expressed: “Wide as are the interests of which the
Pruning strategies for mining high utility itemsets
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning
An extensive experimental study with four real-life datasets shows that the resulting algorithm named FHM (Fast High-Utility Miner) reduces the number of join operations by up to 95 % and is up to six times faster than the state-of-the-art algorithm HUI-Miner.
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.