High Utility Itemset Mining Algorithms Research Articles

A Nature-Inspired Method to Mine Top-k Multi-Level High-Utility Itemsets

High-Utility Itemset Mining (HUIM) is designed to discover sets of itemsets that can bring high profits from the database. However, HUIM encounters several challenges in picking a suitable minimum utility threshold for each database. A class of algorithms that select the top- k itemsets based on their utility has been proposed to address this issue. Although traditional top-k HUI mining algorithms do not require a specific threshold, they tend to be very time-consuming and memory-intensive when dealing with large datasets. To tackle the combinational complexity involved in HUIM algorithms, nature-inspired methods have been suggested and adopted. Nonetheless, these algorithms have traditionally focused on handling conventional, often overlooking critical data structures like product hierarchies. Consequently, they fail to extract crucial insight from this novel database format. Thus, our research introduces a heuristic-based algorithm designed to leverage top-k itemsets from databases enriched with item taxonomy data. We propose a technique involving the early pruning of unpromising items to enhance mining efficiency. Experimental evaluations are conducted on several datasets to assess the method’s performance, both with and without adopting this strategy, demonstrating its effectiveness.

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 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.

A High Utility Itemset Mining Algorithm Based on Particle Filter

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence.

A Scalable High Utilization Itemset Mining Technique for Large Datasets Using A Bit-Based Model

Objectives: Utility-list based algorithms have gained a lot of traction due to their efficiency and the ease with which they may be modified. While there have been some enhancements, the problem of inefficiency persists. This research presents a solution to this issue by enhancing the utility-list building process, a crucial function that has received little attention in previous studies. Also, the research aims at reducing memory complexity and better performance than existing approaches. Methods: To expedite building, a new set of bitwise operations termed Bit combine construction (BCC) is proposed. In addition, BCC is supported by a unique data format called EBP (Efficiency Bit Partition). An innovative EBP-Miner algorithm is developed with this framework in mind, and it uses many techniques to narrow the search field. Findings: On widely used baseline methods, experimental findings reveal that EBP-Miner outperforms numerous state-of-the-art techniques, including FEACP as well as CLH-Miner approaches. The experiments were conducted with utilization value ranging from 20% to 100% of the nodes. The proposed system achieves an average of 390s runtime and utilization value of 90.25% which are outperformed the existing methods. Also, the approach has proven 20% lesser memory complexity than of the existing algorithms. Novelty: In the field of data mining, high utility itemset mining (HUIM) is an important challenge. The idea is to discover groups of data in a database that are particularly significant or profitable in order to unearth information that can aid in making decisions. The novelty of this study is on developing a better method for building algorithms for HUIM that make use of a bitwise data structure, and on suggesting a more time- and effort-effective strategy for building utility-lists. Keywords: High Utility Itemsets; Data Mining; Optimization Model; Bitwise Operations; Pattern Mining

Mining high utility itemsets using Genetic Algorithm Based-Particle Swarm Optimization (GA-PSO)

Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets.

An efficient utility-list based high-utility itemset mining algorithm

An efficient utility-list based high-utility itemset mining algorithm

High-utility itemsets mining based on binary particle swarm optimization with multiple adjustment strategies

As an essential data mining task, high-utility itemset mining (HUIM) has attracted a lot of research. With the increase of dataset size, traditional exact HUIM algorithms are faced with exponential growth search space, which is unacceptable for some algorithms. As HUIM can be treated as a combinatorial optimization problem, Evolutionary Computation (EC) based HUIM approaches have been proposed and shown promise performance in mining HUIs. However, the existing EC-based HUIM approaches usually only find part of the HUIs in a limitation time or discovering all the HUIs is usually time-consuming. In this study, an improved binary particle swarm optimization for HUIM (HUIM-IBPSO) is proposed with multiple adjustment strategies to address these problems. In HUIM-IBPSO, a particle movement direction adjustment strategy is presented to keep the same HUIs during the evolution process. In order to utilize the repeated HUIs more efficiently and enhance the search ability, the strategy of local exploration is proposed in HUIM-IBPSO. A restart strategy for the population is developed in HUIM-IBPSO with the purpose to avoid the premature convergence before discovering any HUIs. To mine HUIs more efficiently, particle modify strategy and fitness value hash strategy are introduced in HUIM-IBPSO. A comprehensive comparison with five state-of-the-art EC-based HUIM algorithms and three precise HUIM algorithms on real datasets shows that the designed model outperforms them in terms of the number of HUIs found, the speed at which they converge, and the duration of execution (runtime).

H-Map-Based Technique for Mining High Average Utility Itemset

High Utility Itemset Mining (HUIM) is the process of locating itemsets that are profitable and useful to users. One of the key flaws in HUIM is that as the length of the itemset increases, the utility also increases. The true utility/profit of the itemset is not revealed in HUIM. High Average Utility Itemset mining overcomes the limitations of HUIM by taking the length of the itemset into account when estimating the utility. Existing pruning methods used for eliminating weak candidates overestimate the average usefulness of itemsets, causing the mining process to slow down. To prevent processing unpromising candidate itemsets and efficiently reduce the search space and processing time, the proposed methodology employs Upper Bound using Remaining Items Utility, Maximum Itemset Utility, and Sum of Maximum Utility in a Transaction. It also uses the multithreaded parallel approach to reduce the processing time. The H-Map-based data structure (H-Map) used for storing the utility values reduces the lookup time and joins used for itemset extension compared to existing state-of-the-art High Average Utility Itemset mining algorithms. The performance of the proposed work is evaluated in terms of memory usage and the time taken for processing. The proposed work increases the overall efficiency of the system employing effective pruning algorithms for pruning poor candidate itemsets and an efficient data structure for storing utility values.

Design and Implementation of Automatic Selection of the Most Efficient Itemset Algorithm Based on Spark

The combination of Spark distributed platform and High-Utility Itemset Mining can solve the problem of long running time issue of High-Utility Itemset Mining. In the experiment, we conclude that Spark-based parallel D2HUP and EFIM algorithms have a greater improvement in running time efficiency than serial algorithms. The existing research has shown that the EFIM and D2HUP algorithms are the two best algorithms for High-Utility Itemset Mining. This paper generates 118 datasets by generating and collecting the running time of the two algorithms in the real and simulated datasets, taking into account the characteristics of each dataset’s length, sparse degree, and dataset size as characteristics with running time as the prediction target and then establishing a model. The accuracy of the prediction was evaluated through experiments, and a set of rules based on decision trees was generated. According to the rules, the fastest algorithm between EFIM and D2HUP can be predicted very well.

High Utility Item-Set Mining From Retail Market Data Stream With Various Discount Strategies

High Utility Item-set Mining (HUIM) is the futuristic remodel version of Frequent Item Mining (FIM). It has the ability to discover customer purchase trends in the retail market. By using that knowledge, retailers can incorporate innovative schemes (discounts, cross-marketing, seasonal sales offers,... etc) to enhance profit. Even though many HUIM algorithms are proposed to detect profitable patterns, most of them cannot be applied to all kinds of retail market data sets due to certain assumptions. The first assumption is that the items always produce a positive profit. But in reality, even though overall profit could be positive, some of the items make negative profit. The second one is they are developed for static transactional data. Those are useful to take decisions at some intervals like quarterly, half-yearly, yearly. But, to take decisions at any time by analyzing the present sales trend, it is required to process the data stream.

UBP-Miner: An efficient bit based high utility itemset mining algorithm

HUIM (High utility itemset mining) is a key problem in data mining. The goal is to find itemsets having a high importance or profit in a database, to identify useful knowledge that can support decision-making. In recent years, many HUIM algorithms have been put forward. Among them, utility-list-based algorithms have become very popular as they are easily extendable and efficient. Although several improvements were made, efficiency remains a critical issue. To address this problem, this paper proposes to improve the utility-list construction process, a key operation that has not been much studied in prior work. A novel set of bitwise operations is proposed called BEO (Bit mErge cOnstruction) to speed up the construction process. Besides, a novel data structure called UBP (Utility Bit Partition) is designed to support BEO. This structure is integrated into a novel UBP-Miner algorithm, which also applies several search space reduction strategies. Experimental results show that UBP-Miner is faster than several state-of-the-art algorithms such as HUI-Miner* and ULB-Miner on common benchmark datasets.

Compact Tree Structures for Mining High Utility Itemsets

High Utility Item set Mining (HUIM) from large transaction databases has garnered significant attention as it accounts for the revenue of the items purchased in a transaction. Existing tree-based HUIM algorithms discard unpromising items and require at most two database scans for their construction. Hence, whenever utility threshold is changed, the trees have to be reconstructed from scratch. In this regard, the current study proposes to not only incorporate all the items in the tree structure but compactly represent transaction information. The proposed trees namely- Utility Prime Tree (UPT), Prime Cantor Function Tree (PCFT), and String based Utility Prime Tree (SUPT) store transaction-level information in a node unlike item-based prefix trees. Experiments conducted on both real and synthetic datasets compare the execution time and memory of these tree structures with a proposed Utility Count Tree (UCT) and existing IHUP, UP-Growth trees. Due to transaction-level encoding, these structures consume significantly less memory when compared to the tree structures in the literature.

EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data

High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item, and the algorithm objectives to locate the set of items with a utility higher or equivalent to a set benchmark. These itemsets are utilized to build association rules for data mining systems. However, in the age of big data, conventional (HUIM) strategies are least effective with limited processing capabilities. This work proposes an optimized technique, Enhanced Absolute High Utility Itemset Miner (EAHUIM) by incorporating various refinements into the Absolute High Utility Itemset Miner (AHUIM) Algorithm. EAHUIM discovers the itemsets from large datasets in near real-time and serves as the foundation for information management and decision-making systems by providing diverse insights. The experimental analysis reveals that EAHUIM outclasses other state-of-the-art algorithms for HUIM.

TKN: An efficient approach for discovering top-k high utility itemsets with positive or negative profits

Top-k high utility itemsets (HUIs) mining permits discovering the required number of patterns - k, without having an optimal minimum utility threshold (i.e., minimum profit). Multiple top-k HUIs mining algorithms have been introduced with interesting results. However, these algorithms focus mainly on mining patterns from positive profit datasets, while few preliminary studies can handle datasets with negative profits. Moreover, conventional top-k HUI mining algorithms, that are meant for exploring positive profit datasets, perform poorly when mining top-k HUIs on highly dense and large datasets. In this paper, we propose TKN (efficiently mining Top-K HUIs with positive or Negative profits) which employs generalized and adaptive techniques to mine both positive and negative profit datasets effectively. The proposed approach adopts transactional projection and merging mechanisms to decrease the dataset traversing cost. Furthermore, several pruning and threshold elevating ideas are utilized to significantly narrow the exploration space. To highlight the reliability of the devised TKN, a series of extensive comparisons were conducted using two versions of six real datasets. The obtained results reveal that TKN is clearly superior in finding the required number of patterns, whether on positive or negative profit datasets, compared to the current cutting-edge competitors.

Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to discover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based databases. It can be seen from a thorough performance evaluation that FEACP can identify useful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse databases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.

Mining High Utility Itemsets with Hill Climbing and Simulated Annealing

High utility itemset mining (HUIM) is the task of finding all items set, purchased together, that generate a high profit in a transaction database. In the past, several algorithms have been developed to mine high utility itemsets (HUIs). However, most of them cannot properly handle the exponential search space while finding HUIs when the size of the database and total number of items increases. Recently, evolutionary and heuristic algorithms were designed to mine HUIs, which provided considerable performance improvement. However, they can still have a long runtime and some may miss many HUIs. To address this problem, this article proposes two algorithms for HUIM based on Hill Climbing (HUIM-HC) and Simulated Annealing (HUIM-SA). Both algorithms transform the input database into a bitmap for efficient utility computation and for search space pruning. To improve population diversity, HUIs discovered by evolution are used as target values for the next population instead of keeping the current optimal values in the next population. Through experiments on real-life datasets, it was found that the proposed algorithms are faster than state-of-the-art heuristic and evolutionary HUIM algorithms, that HUIM-SA discovers similar HUIs, and that HUIM-SA evolves linearly with the number of iterations.

ETKDS: An efficient algorithm of Top-K high utility itemsets mining over data streams under sliding window model

The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDHSW structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets.

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers have proposed algorithms to efficiently mine highly profitable itemsets in customer transaction databases, in which the unit profits of items are fixed. However, this assumption does not reflect the true nature of the utility measure of items in real-life transaction databases, which might vary over time. Moreover, since this important characteristic is ignored by all the current HUI mining algorithms, they are either not applicable to this type of database, or they generate inaccurate results. In addition, the HUI mining algorithms’ traditional limitation is that they produce a huge number of HUIs for users. In this paper, we define the problem of mining a lossless, concise and compact representation of HUIs, called closed HUIs (CHUIs), in dynamic unit profit databases. Based on newly defined of utility measure, a novel algorithm, called iEFIM-Closed, is introduced. This relies on this new utility measure, a novel compact database format to reduce the cost of database scans and increase the efficiency of the mining process. Experimental evaluations show that iEFIM-Closed significantly outperforms state-of-the-art algorithms for mining CHUIs on sparse databases with dynamic profit, and has competitive performance in dense databases in terms of runtime, the cost of database scans and scalability.