High Utility Itemsets Research Articles

SOHUPDS+: An Efficient One-phase Algorithm for Mining High Utility Patterns over a Data Stream

Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose one-phase algorithm SOHUPDS+ to mine high utility itemsets in the current sliding window of the data stream with respect to absolute or relative minimum utility threshold. To facilitate SOHUPDS+, we propose a data structure IUDataListSW+ , which stores and maintains utility and upper-bound values of the items in the current sliding window when sliding window advances. In addition, we propose a transaction merging strategy, called BitmapTransactionMerging , which saves execution time for utility and upper-bound values computations in denser datasets. Moreover, we propose update strategies to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. The results of experiments illustrate that SOHUPDS+ is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage in most of the experiments on various datasets.

Retraction Note: High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

Retraction Note: High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

Modified Genetic Algorithm for Efficient High-Utility Itemset Mining

In pattern mining, high-utility itemset mining (HUIM) is useful for discovering high-utility patterns. The study of HUIM using heuristic techniques reflects issues in producing better offspring. It is ineffective in terms of search space organization, population diversity, and utility calculation, which impact runtime and accuracy. It is observed that very few researchers have experimented with genetic algorithm (GA) and are still facing the same issues as mentioned before. To overcome these problems, a novel approach is proposed for HUIM using modified GA and optimized local search (HUIM-MGALS) with six potential contributions. First is linking the utility with the Bitmap dataset to reduce utility access time, leading to effective search space organization. Second, HUIM-MGALS employs a fitness scaling strategy to avoid redundancy. Third, a high-utility itemset (HUI) revision strategy is employed to explore significant HUIs. Modified population diversity maintenance strategy and iterative crossover help to preserve significant HUIs and improve search capability as fourth and fifth contributions. Sixth, the use of multiple mutations refines the wasted individuals to boost accuracy. Extensive experimentation showed that HUIM-MGALS significantly outperforms the presented algorithms, up to 8.6 times faster. It also demonstrates superior HUI discovery capabilities for both sparse and dense datasets. This is supported by the modified population diversity maintenance strategy, which is proved to be the most impactful modification for HUI discovery in HUIM-MGALS.

IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments

Periodic high-utility itemset (PHUI) mining can extend beyond the conventional approach of high-utility itemset mining by uncovering recurring customer purchase behaviors common in real-life scenarios (e.g., buying apples and oranges every three days or weekly). Such behaviors, particularly in market basket databases, signify stable patterns that ensure long-term profitability. Existing PHUI mining algorithms assume a static database and incur significant costs when handling incremental databases, as each batch of new transactions necessitates reprocessing the entire dataset. To overcome this challenge, we introduce the Incremental Periodic High-Utility Itemset Miner (IPHM), a method for efficiently extracting periodic high-utility itemsets in incremental database environments. We propose an innovative incremental utility-list structure tailored for incremental database scenarios. Effective pruning strategies are employed to expedite the construction and update of incremental utility-lists and to discard unpromising candidates. As demonstrated by the experimental results, the algorithm is efficacious and efficient, highlighting its practical applicability in dynamic data environments. The algorithm shows a remarkable ability to quickly adapt to database changes, making it highly suitable for applications in market basket analysis where frequent updates are common.

PHUI-GA: GPU-Based Efficiency Evolutionary Algorithm for Mining High Utility Itemsets

PHUI-GA: GPU-Based Efficiency Evolutionary Algorithm for Mining High Utility Itemsets

Product Layout Analysis Based on Consumer Purchasing Patterns Using Apriori Algorithm

In every self-service store, it is certain to have a sales transaction data, where the data will continue to grow every day. But in self-service stores the data is only a record of sales at the store. Whereas transaction data can be used as information on how consumer purchasing patterns when shopping at the store, but not all supermarkets know this. So this research aims to find information on these purchasing patterns, where to do this research using the apriori algorithm which is part of the association technique which is also part of data mining, where in its application it will calculate the support value, confindence value and will be tested using the lift ratio. And after the calculation is carried out, optimization will be carried out using the high utility itemset mining variable which will calculate the highest profit value on the product, so that based on the calculation, the final result is obtained with a support value of 85%, a confidence value of 86%, a lift ratio test of 1.01 and the high utility gets the highest result of Rp. 567,000.

High-utility itemsets mining integrating an improved crow search algorithm and particle search optimization

High-utility itemsets mining integrating an improved crow search algorithm and particle search optimization

Correlated time-window constrained high-utility itemsets mining with certain and uncertain real-life datasets

Correlated time-window constrained high-utility itemsets mining with certain and uncertain real-life datasets

MỘT THUẬT TOÁN HỮU ÍCH ĐỂ KHAI THÁC TẬP HỮU ÍCH CAO

High utility itemsets (HUIs) mining is the finding of itemsets that satisfy a user-defined minimum utility threshold. Many successful studies in this field have been carried out, however they are all reliant on Tidset techniques, which records the intersection of transactions in a data structure. This paper presents the DCHUIM algorithm which mines the high utility itemset based on the Diffset techniques. Essentially, this mechanism stores the subtraction set of transactions rather than the intersection set. In order to achieve this, a DUL data structure is proposed to store utilities information and subtraction transactions of an itemset. Furthermore, the algorithm also applies pruning strategies such as U-Prune, EUCS-Prune and the concept of closed utility to effectively compress data. Thus, in the mining process, the search space is greatly diminished. Experiment on large datasets including Accidents, Mushroom, Retail, Chainstore and compare the performance of DCHUIM algorithm with HMiner algorithm. The findings indicate that the DCHUIM method outperforms the HMiner algorithm in terms of memory utilization across all databases and outperforms it in terms of time on sparse databases.

Cross-Entropy Assisted Optimization Technique for High Utility Itemset Mining from the Transactional Database

Cross-Entropy Assisted Optimization Technique for High Utility Itemset Mining from the Transactional Database

TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets

TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets

An efficient strategy for mining high-efficiency itemsets in quantitative databases

The classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does not address the issue of the cost price of the products. Therefore, neither can locate products with high-efficiency value on investment. To overcome these problems, the high-efficiency itemset mining (HEIM) problem was proposed. Despite its practicality, this issue has received little attention. The algorithms proposed to exploit high-efficiency itemsets (HEI) still use ineffective strategies on dense databases and unstrict upper bounds, requiring a lot of time and memory. To address the current issues with HEIM, the paper proposes tight upper bounds for the early pruning of candidates. Several techniques are also proposed, such as combining similar transactions and saving promising transaction locations, to reduce the cost of database scanning. Finally, the techniques are combined to propose a novel way to implement the MHEI (an efficient strategy for Mining High-Efficiency Itemsets in quantitative databases) to optimize the HEIM process. The experimental process also shows that the proposed algorithm has performance better than the state-of-the-art algorithm in HEIM.

Retraction: KLogeswaran, PSuresh. High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation. Comput Intell38: 1596–1615, 2022 (10.1111/coin.12490)

The above article, published online on 14 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest‐edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

MMC: efficient and effective closed high-utility itemset mining

MMC: efficient and effective closed high-utility itemset mining

High Utility Itemset Extraction using PSO with Online Control Parameter Calibration

This study investigates the use of evolutionary computation for mining high-value patterns from benchmark datasets. The approach employs a fitness function to assess the usefulness of each pattern. However, the effectiveness of evolutionary algorithms heavily relies on the chosen strategy parameters during execution. Conventional methods set these parameters arbitrarily, often leading to suboptimal solutions. To address this limitation, the research proposes a method for dynamically adjusting strategy parameters using temporal difference approaches, a machine learning technique called Reinforcement Learning (RL). Specifically, the proposed IPSO RLON algorithm utilizes SARSA learning to intelligently adapt the Crossover Rate and Mutation Rate within the Practical Swarm Optimization Algorithm. This allows IPSO RLON to effectively mine high-utility itemsets from the data.The key benefit of IPSO RLON lies in its adaptive control parameters. This enables it to discover optimal high-utility itemsets when applied to various benchmark datasets. To assess its performance, IPSO RLON is compared to existing approaches like HUPEUMU-GRAM, HUIM-BPSO, IGA RLOFF, and IPSO RLOFF using metrics like execution time, convergence speed, and the percentage of high-utility itemsets mined. From the evaluation it is observed that the proposed IPSO RLON perfroms better than the other methodology.

CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets

CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets

Incremental high average-utility itemset mining: survey and challenges

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

Mining Top-k High On-shelf Utility Itemsets Using Novel Threshold Raising Strategies

High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional HUIs mining because it deals with on-shelf-based time period and negative utility of items. Moreover, most algorithms need minimum utility threshold ( min_util ) to find rules. However, specifying the appropriate min_util threshold is a difficult problem for users. A smaller min_util threshold may generate too many rules and a higher one may generate a few rules, which can degrade performance. To address these issues, a novel top-k HOUIs mining algorithm named TKOS ( T op- K high O n- S helf utility itemsets miner) is proposed which considers on-shelf time period and negative utility. TKOS presents a novel branch and bound-based strategy to raise the internal min_util threshold efficiently. It also presents two pruning strategies to speed up the mining process. In order to reduce the dataset scanning cost, we utilize transaction merging and dataset projection techniques. Extensive experiments have been conducted on real and synthetic datasets having various characteristics. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms. The proposed algorithm is up to 42 times faster and uses up-to 19 times less memory compared to the state-of-the-art KOSHU. Moreover, the proposed algorithm has excellent scalability in terms of time periods and the number of transactions.

Mining Top-K constrained cross-level high-utility itemsets over data streams

Mining Top-K constrained cross-level high-utility itemsets over data streams