High Utility Itemset Mining Research Articles

A survey of high utility sequential patterns mining methods

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given.

Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches.

Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.

Mining High Utility Itemsets Using Prefix Trees and Utility Vectors

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> items generally results in a huge search space, composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

A Reinduction-Based Approach for Efficient High Utility Itemset Mining from Incremental Datasets

High utility itemset mining is a crucial research area that focuses on identifying combinations of itemsets from databases that possess a utility value higher than a user-specified threshold. However, most existing algorithms assume that the databases are static, which is not realistic for real-life datasets that are continuously growing with new data. Furthermore, existing algorithms only rely on the utility value to identify relevant itemsets, leading to even the earliest occurring combinations being produced as output. Although some mining algorithms adopt a support-based approach to account for itemset frequency, they do not consider the temporal nature of itemsets. To address these challenges, this paper proposes the Scented Utility Miner (SUM) algorithm that uses a reinduction strategy to track the recency of itemset occurrence and mine itemsets from incremental databases. The paper provides a novel approach for mining high utility itemsets from dynamic databases and presents several experiments that demonstrate the effectiveness of the proposed approach.

A Method Mining Correlation High Utility Itemsets with Negative Profits

High-utility itemset (HUI) mining is an important task in the data mining process. Recently, many algorithms have been proposed to mine sets of HUIs. Most algorithms only work with data sets with positive return values. However, in the real world, the set of business items often includes both positive (+) and negative (-) profit values. To solve this problem, an algorithm is needed to find HUIs with negative returns in the database.The issue is how to mine efficiently correlation high utility itemsets with negative profits. Given the transactional database D and the profit table of products, how to find the best correlation high utility itemsets on the database with items having negative profits. To address these issues, the paper proposes a COHUIs_CoHUN method to mine correlation high utility itemsets with negative profits

High utility itemsets mining from transactional databases: a survey

High utility itemsets mining from transactional databases: a survey

Targeted mining of top-[formula omitted] high utility itemsets

Finding high-importance patterns in data is an emerging data mining task known as High-utility itemset mining (HUIM). Given a minimum utility threshold, a HUIM algorithm extracts all the high-utility itemsets (HUIs) whose utility values are not less than the threshold. This can reveal a wealth of useful information, but the precise needs of users are not well taken into account. In particular, users often want to focus on patterns that have some specific items rather than find all patterns. To overcome that difficulty, targeted mining has emerged, focusing on user preferences, but only preliminary work has been conducted. For example, the targeted high-utility itemset querying algorithm (TargetUM) was proposed, which uses a lexicographic tree to query itemsets containing a target pattern. However, selecting the minimum utility threshold is difficult when the user is not familiar with the processed database. As a solution, this paper formulates the task of targeted mining of the top-k high-utility itemsets and proposes an efficient algorithm called TMKU based on the TargetUM algorithm to discover the top-k target high-utility itemsets (top-k THUIs). At the same time, several pruning strategies are used to reduce memory consumption and execution time. Extensive experiments show that the proposed TMKU algorithm has good performance on real and synthetic datasets.

An efficient method for mining High-Utility itemsets from unstable negative profit databases

The study of High-Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM) is crucial since it explains consumer behavior and offers actionable advice to improve business results. HUIM algorithms have been successfully established to identify high-utility itemsets, including those with negative utilities. The problem with these approaches is that they presume incorrectly that items with negative utility across transactions would always be losses. Products with positive profitability may seem negative when combined with other items to increase sales or reduce inventory. Using strict upper-bound approaches, this paper presents strategies for making database scanning more efficient and reducing the number of prospective candidates. We also prove that it is correct to use the proposed upper-bounds for pruning on several types of items in the database. Based on all the proposed solutions, we develop a novel algorithm to solve this problem efficiently. To demonstrate their efficiency, the algorithms are tested against states-of-art HUIM algorithm on diverse datasets with regard to size and characteristics with unstable negative profits.

A residual utility-based concept for high-utility itemset mining

A residual utility-based concept for high-utility itemset mining

RSPHUIM: Recent Short Period High Utility Itemset Mining

RSPHUIM: Recent Short Period High Utility Itemset Mining

Parallel approaches to extract multi-level high utility itemsets from hierarchical transaction databases

In the field of data mining, high utility itemset mining (HUIM) is a relevant mining task, with the aim of analyzing customer transaction databases. HUIM consists of exploiting the set of items that are often purchased together and yield high profit value. In real-world applications, transaction databases often come with item categorization, stored in a taxonomy. Items in these databases can be clustered into specific categories at higher levels of abstraction. Extracting and analyzing itemsets discovered from different levels of abstraction can provide more useful insights into customer behaviors. However, considering item taxonomy increases the problem’s complexity, hence prolonging the execution time needed to explore the search space. Parallelism is thus employed to address this drawback, but previous approaches are not efficient as they only adopt simple scheduling strategies or do not utilize the full capabilities of a multi-core processor. This work introduces three new efficient strategies to significantly boost the performance of the multi-level high utility itemset mining task using multi-core processing. Two new algorithms, called MCML+ and MCML++, are also proposed by adopting the suggested strategies. Extensive experiments on several large databases show that our proposed algorithms have better performance compared to previous approaches in terms of running time and scalability, up to 4.0 times better than the previous parallelized algorithm, the MCML-Miner algorithm; and over 9.0 times faster than the original sequential algorithm, the MLHUI-Miner algorithm.

An Examination of High Utility Item Set Mining using Different Techniques

An Examination of High Utility Item Set Mining using Different Techniques

A Privacy Frequent Itemsets Mining Framework for Collaboration in IoT Using Federated Learning

Rapid advancement of industrial internet of things (IoT) technology has changed the supply chain network to an open system to meet the high demand for individualized products and provide better customer experiences. However the open-system supply chain has forced many small and midsize enterprises (SMEs) to adopt vertical integration by being divided into smaller companies with a distinctive business for each SME but a central alliance to produce a range of products and gain competencies. Therefore, existing models do not guarantee the protection of data privacy of individual SMEs. Moreover, especially for the IoT environment, collecting data in a secure way and revealing valuable knowledge in an IoT network is difficult. How to share data in a secure framework is of paramount importance in the internet of behavior field. In this article, a privacy-preserving data-mining framework is proposed for joint-venture industrial collaborative activities by combining federated learning and a “pre-large concept” of data-mining techniques. The novelty of the proposed approach is that, while mining high-utility itemsets (HUIs) from multiple datasets, it does not require direct data sharing. In the proposed method, the federated-learning framework can learn from aggregated learning parameters without scanning all data from different sets. The pre-large concept in this approach reduces the amount of scanning into different datasets. Thus, the approach makes it possible to train federated learning more quickly while protecting the privacy of individual data owners. The approach has been tested on real industrial datasets in a collaborative environment. Extensive experimental results show that the approach achieves high accuracy compared with conventional data-mining techniques while preserving the privacy of datasets.

Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

High-utility itemset mining (HUIM) can consider not only the profit factor but also the profitable factor, which is an essential task in data mining. However, most HUIM algorithms are mainly developed on a single machine, which is inefficient for big data since limited memory and processing capacities are available. A parallel efficient high-utility itemset mining (P-EFIM) algorithm is proposed based on the Hadoop platform to solve this problem in this paper. In P-EFIM, the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework. Then the ordered itemsets are renumbered, and the low-utility itemsets are pruned to improve the dataset utility. In the Map phase, the P-EFIM algorithm divides the task into multiple independent subtasks. It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing. Furthermore, the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase. Experiments are performed on eight datasets, and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth, which is also HUIM algorithm based on the Hadoop framework.

Optimization of search space division with enhanced shared memory-based utility list buffer miner with a parallel framework (SM-PLB) for effective high utility itemset mining

Optimization of search space division with enhanced shared memory-based utility list buffer miner with a parallel framework (SM-PLB) for effective high utility itemset mining

Enhanced Differential Evolution and Particle Swarm Optimization Approaches for Discovering High Utility Itemsets

Mining patterns from High-utility itemsets (HUIs) have been exploited recently in place of frequent itemset mining (FIMs) or association-rule mining (ARMs) as they highlight profitability of products where quantity and profits are taken into account. Several techniques for HUIs have been proposed and they encounter exponential search spaces which have more distinct items or voluminous databases. Alternatively, Evolutionary Computations (ECs)-based meta-heuristics algorithms can be effective in solving issues in HUIs since a set of near-optimal solutions can be obtained within restricted periods. Current ECs-based techniques consume more time to identify HUIs in transactional databases, discover unacceptable combinations of HUIs, and finally fail to discover HUIs when neighborhood searches are not executed locally and globally. To overcome these challenges, a HUI mining algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) using multiple strategies including elitism, population diversifications, exclusive preservations, and neighborhood exploration techniques has been proposed. Thus, this work defines mining patterns based on DE and PSO to identify HUIs in voluminous transactional databases. The HUIM-DE-PSO-DE algorithm proposed in this work discovers more number of HUIs which is revealed in experimental results obtained from a set of benchmark data instances. Results are compared with existing approaches using several performance metrics including convergence speeds, minimum utility threshold values, and execution time consumed.

FCHM-stream: fast closed high utility itemsets mining over data streams

The high-speed, continuous and endless characteristics of data streams make it a challenging task to quickly mine high utility itemsets in limited memory space. The sliding window model, which focuses only on the most recent data, has received extensive research and attention as it can effectively adapt to the data stream environment. However, the presence of many communal batches in adjacent sliding windows causes the algorithm to repeatedly generate a large number of identical itemsets, which reduces the spatiotemporal performance of the algorithm. In order to solve these problems and provide users with a concise and lossless resultset, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream. A new utility list structure based on batch division and a resultset maintenance strategy based on skip-list structure are designed to effectively reduce identical itemsets repeatedly generated and thus reduce the running time of the algorithm. Extensive experimental results show that the proposed algorithm has a large improvement in runtime compared to the state-of-the-art algorithms.