FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

Wei, Tianyou; Wang, Bin; Zhang, Yuntian; Hu, Keyong; Yao, Yinfeng

doi:10.1109/access.2020.3001975

Cited by 19 publications

(13 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wei et al 20 stated a novel approach to eliminate the time computational complexity and memory complexity. The proposed FCHUIM did not perform repeated scanning of the database and was used to sort and retrieve utility lists.…”

Section: Literature Surveymentioning

confidence: 99%

High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

Kumar¹,

Rao

2022

Concurrency and Computation

View full text Add to dashboard Cite

High utility itemsets (HUIs) are items in the dynamically streaming transaction list that generate a high‐profit margin. Many of the real‐time applications depend on finding HUIs from the transaction list. However, HUI mining is time‐consuming and results in high complexity due to memory requirements, a large search space, and the cost of HUI estimation. To overcome these issues we have proposed a novel High Median Utility Itemset Mining (HMUIM) approach. This approach utilizes the HMUI‐Miner which effectively ignores the unnecessary itemsets, that is, items with less profit, and mines the HUIs from the database. Furthermore, it has the limitation of deleting the transaction list based on the sliding window size used while conducting a dynamic streaming dataset. To tackle this issue we proposed a novel Modified Heap‐based Optimizer (MHBO) algorithm which effectively copies the HUIs transaction list and preserves it for further process. The MHBO is the combination of Heap Based Optimizer and nine fuzzy rules. The fuzzy rules are used to analyze the priority of the transaction list and based on that it ignores the transaction list with low priorities. Experimental analysis is performed for the proposed method on the real‐time dataset and compared with HUI and HAUIM approaches. The proposed method reduces the execution time and memory usage based on the minimum threshold. Meanwhile, the proposed MHBO approach is compared with iMEFIM, REX, and FCHUIM state‐of‐art works based on the sliding window concept. The execution time to store the replicated copies of original data is less for our proposed method.

show abstract

Section: Literature Surveymentioning

confidence: 99%

High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

Kumar¹,

Rao

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…For example, in the retail dataset, we can explore regular purchases items which have high profit. Close and maximal HUI are compact representation of HUIM [29,31,33]. It helps to decrease the number of candidate itemset.…”

Section: Other Variationsmentioning

confidence: 99%

“…To solve that problem, many research have been proposed such as Apriori based [7,8], tree based [9][10][11][12][13][14][15][16][17], utility list based [18-23, 26-31, 33, 34, 36-38] and hybrid based algorithm [24,25]. Moreover, there are several HUIM variations such as high average utility itemset mining (HAUIM) [16,35], HUIM in incremental databases [17,32], HUIM in sequential database (HUSPM) [18-20, 37, 38], HUIM in regular occurrence [34], Close HUIM [29,31,33], and correlated HUIM [28,30]. These kinds of HUIM are introduced to make the results more meaningful.…”

Section: Introductionmentioning

confidence: 99%

A Review of High Utility Itemset Mining for Transactional Database

Atmaja

Sonawane²

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

High utility itemset mining (HUIM) is an expansion of frequent itemset mining (FIM). Both of them are techniques to find interesting patterns from the database. The interesting patterns found by FIM is based on frequently appeared items. This approach is not that efficient to identify the desired patterns, as it considers only existence or non existence of items in database and ignores utility. Whereas the patterns are more meaningful for the user if the utility is considered. The utility can be quantity, profit, cost, risk or other factors based on user interest. HUIM is another approach to find interesting patterns by considering utility of items along with the frequency. It uses minimum utility threshold to determine if an itemset is high utility itemset (HUI) or not. There are several challenges to implement utility from traditional pattern mining to HUIM. Lately, there are many research contributions that proposed different algorithms to solve these issues. This review work explores various HUIM techniques with detailed analysis of different strategies like Apriori, Tree based, Utility Lists based, and Hybrid. These strategies are used to implement various HUIM techniques in order to achieve the effectiveness in pattern mining. The observations and analytical findings based on this detailed review done with respect to various parameters can be recommended and used for further research in the pattern mining.

show abstract

“…High Utility Itemset Mining (HUIM) refers to extracting all itemsets that exceed a predefined minimum utility threshold minUtil set by the user using a utility function [4], [5]. HUIM has been extensively used for process model extraction in different applications, such as recommendation systems, retail market analysis, and medical applications [6], [2]. Furthermore, the model's performance has been improved by developing a set of pruning strategies and data structures to reduce the search space and significantly speed up the mining process [7], [8].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach for Mining Reliable High Utility Patterns

Fouad¹,

Hussein

Rady

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold. However, a significant number of patterns will be generated if this threshold is set too low, which is an inherent limitation of these algorithms. This may cause the mining process to be inefficient as it would be difficult to analyze the patterns found. Furthermore, most of these patterns are unreliable and hard to be employed in making decisions. This paper proposed a novel problem of mining reliable high utility patterns by adapting the concept of reliability to mine a significant type of pattern called reliable high utility patterns. To address this issue, an efficient approach named RUPM (Reliable Utilitybased Pattern Mining) is presented. RUPM introduces three novel measurements for estimating the reliability of utility-based patterns and proposes several strategies to efficiently handle reliable patterns with high utility values. Experimental results suggest that up to 99% of the patterns discovered by existing traditional high utility pattern mining algorithms were, in fact, unreliable. In contrast, the average reliability proportion in the resultant patterns obtained from the RUPM approach is at least 47.6% higher. Moreover, the proposed pruning strategies provide a reduction in both the runtime and memory usage.INDEX TERMS Data mining, reliable high-utility itemset, utility mining, pruning strategy.

show abstract

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

Cited by 19 publications

References 40 publications

High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

A Review of High Utility Itemset Mining for Transactional Database

An Efficient Approach for Mining Reliable High Utility Patterns

Contact Info

Product

Resources

About