A TIFF-Tree Based High Utility Itemset Mining Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1713-1717
Author(s):  
Yi Pan ◽  
Bo Zhang
Keyword(s):  
Two Phase ◽  
Column Operation ◽  
Itemset Mining ◽  
Mining Algorithm ◽  
Utility Information ◽  
High Utility ◽  
High Utility Itemsets ◽  
Result Analysis ◽  
Better Than

Owing to their major contribution to the total transaction's sales profits, increasingly importance has been attached to high utility itemsets mining. This paper has proposed a TIFF-tree based algorithm, which takes two-pass database scan to obtain the transaction utility information, the conditional matrix of potential high utility is adopted, through the row-column operation, the calculation of transaction utility can be simplified. The experiment result analysis shows that as the decreasing of user-defined threshold, the performance of TIFP-Growth algorithm is much better than the two-phase algorithm.

Mining of top-k high utility itemsets with negative utility

2020 ◽  
pp. 1-16
Author(s):  
Rui Sun ◽  
Meng Han ◽  
Chunyan Zhang ◽  
Mingyao Shen ◽  
Shiyu Du
Keyword(s):  
Data Mining ◽  
Search Space ◽  
Experimental Results ◽  
Effective Algorithm ◽  
Memory Usage ◽  
Utility Value ◽  
Itemset Mining ◽  
High Utility ◽  
High Utility Itemsets

High utility itemset mining(HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets(HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.

A high utility itemset mining algorithm based on subsume index

2015 ◽  
Vol 49 (1) ◽  
pp. 315-340 ◽  
Author(s):  
Wei Song ◽  
Zihan Zhang ◽  
Jinhong Li
Keyword(s):  
Itemset Mining ◽  
Mining Algorithm ◽  
High Utility

A Systematic Survey on High Utility Itemset Mining

2019 ◽  
Vol 18 (04) ◽  
pp. 1113-1185 ◽  
Author(s):  
Bahareh Rahmati ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Data Structures ◽  
Search Space ◽  
Frequent Itemset ◽  
Itemset Mining ◽  
Efficient Data ◽  
Average Utility ◽  
High Utility ◽  
High Utility Itemsets ◽  
Downward Closure ◽  
Efficient Data Structures

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.

scholarly journals Phương pháp song song khai phá tập lợi ích cao dựa trên chỉ số hình chiếu

2017 ◽  
pp. 31
Author(s):  
Đậu Hải Phong
Keyword(s):  
Data Mining ◽  
Parallel Algorithms ◽  
Experimental Results ◽  
Sequential Algorithms ◽  
Parallel Method ◽  
Speed Up ◽  
High Utility ◽  
High Utility Itemsets ◽  
Better Than

High utility itemsets (HUIs) mining is one of popular problems in data mining. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. All the parallel algorithms to try reduce synchronization cost and caculation global profit of itemsets. In this paper, we present a parallel method for mining HUIs from projection-based indexing to speed up performance and reduce memory requirements. The experimental results show that the performance and number candidate of our algorithm is better than some non parallel algorithms.

scholarly journals MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY

2020 ◽  
Vol 1 (2) ◽  
pp. 44-47
Author(s):  
Tung N.T ◽  
Nguyen Le Van ◽  
Trinh Cong Nhut ◽  
Tran Van Sang
Keyword(s):  
State Of The Art ◽  
Upper Bounds ◽  
Itemset Mining ◽  
Novel Structure ◽  
Transactional Databases ◽  
Speed Up ◽  
Projection Techniques ◽  
High Utility ◽  
High Utility Itemsets ◽  
Mining Algorithms

The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.

scholarly journals Efficient High Utility Itemset Mining using Utility Information Record

2015 ◽  
Vol 120 (4) ◽  
pp. 34-39
Author(s):  
Prashant V.Barhate ◽  
S. R. Chaudhari ◽  
P. C. Gill
Keyword(s):  
Itemset Mining ◽  
Utility Information ◽  
High Utility

An incremental mining algorithm for high utility itemsets

2012 ◽  
Vol 39 (8) ◽  
pp. 7173-7180 ◽  
Author(s):  
Chun-Wei Lin ◽  
Guo-Cheng Lan ◽  
Tzung-Pei Hong
Keyword(s):  
Incremental Mining ◽  
Mining Algorithm ◽  
High Utility ◽  
High Utility Itemsets
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