Itemset Mining Research Articles

Multi-level high utility-itemset hiding.

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.

Dynamic Association Mining Techniques for the Faster Extraction of High Utility Itemsets from Incremental Databases

Financial and market analysis applications require the mining of strong-utility itemsets. Finding frequent itemsets with high utility patterns is vital for such wide applications. Recent utility-based mining methods were successfully used in the current study to identify high value itemsets from static datasets. Stream databases or incremental databases update the itemsets at regular intervals (schedulers). Incremental Mining-based High Utility Itemset (IM-HUI) algorithms improve the methodologies based on High Utility Itemset (HUI) methods. The proposed technique refines the itemset values and updates the HUIs based on incremental schedulers. It reduces the time and space while mining HUIs over dynamic databases. The efficacy of the proposed work is compared experimentally to that of existing mining techniques on benchmark datasets.

Damped weighted erasable itemset mining with time sensitive dynamic environments

Erasable itemset mining discovers itemsets in product databases with benefits no greater than a designated threshold value. By considering weight constraints and the recency of products in erasable itemset mining, the practitioners can manage the plants more efficiently. However, existing studies on weighted erasable itemset mining do not regard the time-sensitivity of data arrival times. In this paper, we propose a new weighted erasable itemset mining approach considering time-sensitive dynamic environments. For industrial manufacturers with automated control systems, our method focuses on recent data and item weights to effectively filter out unprofitable itemsets. Performance tests show that the proposed method outperforms state-of-the-art studies in runtime with on-par or minimal compromise in memory usage and that it scales capably with varying database sizes. We performed statistical analyses to demonstrate the correctness and significance of the discovered results from our proposed method. Furthermore, extended evaluations on sensitivity and resultant itemsets show how the algorithm responds to varying parameters as well as provide insights on the discovered itemsets.

An efficient PSO-based evolutionary model for closed high-utility itemset mining

An efficient PSO-based evolutionary model for closed high-utility itemset mining

Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy

Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy

A Spatial-Temporal Exploration of Coordination Failures Preceding Coal Mine Explosion Accidents in China

Coal remains a crucial component of China’s energy supply, with production exceeding half of the global output in 2023. Despite safety improvements, the fatality rate in coal mining rose significantly, underscoring ongoing safety challenges. A total of 174 coal mine explosion investigation reports from China between 2000 and 2024 were analyzed, extracting and mining text related to coordination failures. The texts were categorized by time and region, creating two temporal datasets (2000–2018 and 2019–2024) and six regional datasets (Northeast, East, Central South, Southwest, Northwest, and North China). Using frequent itemset mining and social network construction, the concept of risk propagation was applied to identify the critical paths that lead to coal mine explosions. Over time, coordination failures in China’s coal mine explosions have evolved from localized issues among a few stakeholders to complex, multi-layered challenges involving broader governmental oversight and systemic management issues. Based on regional findings, balancing the frequency and severity of penalties, ensuring meaningful safety inspections, and alleviating the policy pressure on small coal mines are key points for addressing coordination failures.

A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold

A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold

DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining

Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments.

Re-induction based mining for high utility item-sets

Re-induction based mining for high utility item-sets

High utility itemset mining in data stream using elephant herding optimization

High utility itemset mining in data stream using elephant herding optimization

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

Optimized Privacy Preserved Itemset Mining using Federated Learning from Transactional Data in Data Mining

Optimized Privacy Preserved Itemset Mining using Federated Learning from Transactional Data in Data Mining

Optimal Keyword Selection by Hybrid Optimization with Itemset Mining for Text Summarization in Biomedical Sector

Biomedical literature is growing exponentially, creating challenges for researchers and clinicians to access relevant information efficiently. Automatic biomedical text summarization is a promising solution to this issue, allowing the extraction of essential information while reducing redundancy. Traditional summarization techniques often rely on shallow linguistic features or simple term frequencies, which fail to capture the complex relationships in biomedical texts. This paper explores advanced methods for biomedical text summarization, including graph-based approaches, frequent item set mining, and deep learning models such as BERT. The proposed framework introduces a hybrid optimization technique combining Colliding Bodies Optimization (CBO) and Cuckoo Search Optimization (CSO) for optimal keyword selection, alongside a Recurrent Neural Network (RNN) for sentence categorization. Extensive experimentation using UMLS-based concept extraction, keyword selection, and Apriori-based itemset mining demonstrates that the proposed method significantly outperforms existing models in both the in formativeness and coherence of generated summaries. The results reveal that combining deep language models with domain-specific knowledge enhances summarization quality and can be effectively applied to diverse types of biomedical text.

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.

Enhancing frequent itemset mining through machine learning and nature-inspired algorithms: a comprehensive review

Enhancing frequent itemset mining through machine learning and nature-inspired algorithms: a comprehensive review

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.

An Improved Apriori Algorithm Based on the Spark Platform

Apriori algorithm is one of the most classical algorithms used to mine frequent term sets, but because of the need to scan the calculation method of transaction set repeatedly, the computational efficiency of the algorithm is seriously reduced and it is difficult to parallelize processing. With the advent of the era of big data, the data scale is increasing. In order to solve this problem, this paper proposes a Apriori parallelization processing method based on vertical data format and Spark computing framework GC-Apriori algorithm. Using the vertical data format, reduce the duplication between things, improve the efficiency of data storage, and the efficiency of frequent item set mining. At the same time, the broadcast variable mechanism of Spark is used to improve the overall computing efficiency. Comparing the performance with other distributed Apriori algorithms on the same scale, the computational efficiency of GC-Apriori algorithm is improved. The results show that the algorithm effectively improves the efficiency of frequent term set mining of Apriori algorithm in distributed environment.

Effective approaches for mining correlated and low-average-cost patterns

High-utility pattern mining is a data-mining research field that focuses on identifying patterns that exceed a specified threshold for utility values. These utility values primarily indicate positive benefits such as profits. However, in actual scenarios, certain applications and demands require the discovery of patterns at a lower cost. Hence, the concept of low-average-cost itemset mining is introduced to evaluate patterns based on their average costs. This is different from high-utility pattern mining, as the cost is minimized. In this study, we focus on extracting patterns with strong correlations and low average costs. Because existing techniques depend on the support metric to devise cost-based pruning strategies, these strategies are ineffective in extracting correlated and low-average-cost patterns. Therefore, we introduce a novel lower bound to efficiently limit the search space for patterns. Experimental analyses are conducted on eight databases to assess the performance of the mining algorithm in terms of execution time, memory usage, pruning effectiveness, and scalability. Additionally, the effects of different pruning techniques and item-sorting orders on the performance of the algorithm are investigated.

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.