scholarly journals Rup/Frup-growth: An Efficient Algorithm for Mining High Utility Itemsets

2017 ◽  
Vol 174 ◽  
pp. 895-903
Author(s):  
Jue Jin ◽  
Shui Wang
Keyword(s):  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

FHN: An efficient algorithm for mining high-utility itemsets with negative unit profits

2016 ◽  
Vol 111 ◽  
pp. 283-298 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Philippe Fournier-Viger ◽  
Wensheng Gan
Keyword(s):  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

An efficient algorithm for hiding sensitive-high utility itemsets

2020 ◽  
Vol 24 (4) ◽  
pp. 831-845
Author(s):  
Vy Huynh Trieu ◽  
Hai Le Quoc ◽  
Chau Truong Ngoc
Keyword(s):  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints

2019 ◽  
Vol 484 ◽  
pp. 44-70 ◽  
Author(s):  
Kuldeep Singh ◽  
Ajay Kumar ◽  
Shashank Sheshar Singh ◽  
Harish Kumar Shakya ◽  
Bhaskar Biswas
Keyword(s):  
Efficient Algorithm ◽  
Utility Value ◽  
High Utility ◽  
High Utility Itemsets

An efficient algorithm for mining temporal high utility itemsets from data streams

2008 ◽  
Vol 81 (7) ◽  
pp. 1105-1117 ◽  
Author(s):  
Chun-Jung Chu ◽  
Vincent S. Tseng ◽  
Tyne Liang
Keyword(s):  
Data Streams ◽  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1078 ◽  
Author(s):  
Thang Mai ◽  
Loan T.T. Nguyen ◽  
Bay Vo ◽  
Unil Yun ◽  
Tzung-Pei Hong
Keyword(s):  
Association Rules ◽  
Business Strategy ◽  
Efficient Algorithm ◽  
Business Managers ◽  
Competitive Strategies ◽  
Computing Systems ◽  
Other Information ◽  
High Utility ◽  
High Utility Itemsets ◽  
The Internet Of Things

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

CHN: an efficient algorithm for mining closed high utility itemsets with negative utility

2018 ◽  
pp. 1-1 ◽  
Author(s):  
Kuldeep Singh ◽  
Shashank Sheshar Singh ◽  
Ajay Kumar ◽  
Harish Kumar Shakya ◽  
Bhaskar Biswas
Keyword(s):  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals An efficient algorithm for mining top-k on-shelf high utility itemsets

2017 ◽  
Vol 52 (3) ◽  
pp. 621-655 ◽  
Author(s):  
Thu-Lan Dam ◽  
Kenli Li ◽  
Philippe Fournier-Viger ◽  
Quang-Huy Duong
Keyword(s):  
Efficient Algorithm ◽  
High Utility ◽  
High Utility Itemsets

Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

2019 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Kuldeep Singh ◽  
Bhaskar Biswas
Keyword(s):  
Data Mining ◽  
Upper Bound ◽  
Efficient Algorithm ◽  
State Of The Art ◽  
Memory Usage ◽  
Important Data ◽  
Benchmark Datasets ◽  
Projection Techniques ◽  
High Utility ◽  
High Utility Itemsets

High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.

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