High Average-utility Itemset Mining Research Articles

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.

Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms.

H-FHAUI: Hiding frequent high average utility itemsets

High average-utility itemset mining consists of analyzing a quantitative customer transactional database to identify high average-utility itemsets (HAUIs), that is sets of items that have a high average utility (e.g. profit). Although important information about customers’ habits can be revealed by HAUIs, they can expose sensitive information. To address this concern, the problem of hiding frequent HAUIs (FHAUIs) is studied, which is to modify a transaction database to ensure that sensitive FHAUIs cannot be discovered. An algorithm is designed, named H-FHAUI, which relies on an extended border approach based on the support (occurrence frequency) and the average-utility of itemsets. Moreover, to hide all FHAUIs, H-FHAUI utilizes a novel extended lower border named BdE- based on weak upper bounds on the average-utility to only hide a small number of FHAUIs. Then, all remaining FHAUIs are also hidden. Besides, H-FHAUI utilizes a novel weight-based strategy named ICS to choose items and transactions to be modified, a novel TIU-VIU structure to quickly update weak upper bounds, and a strategy named DUSWUB to quickly hide FHAUIs while ensuring that the total utility of D is preserved as much as possible. Experimental results show that H-FHAUI outperforms a baseline in terms of runtime, memory usage, and quality of the sanitized database.

Efficient mining high average-utility itemsets with effective pruning strategies and novel list structure

Efficient mining high average-utility itemsets with effective pruning strategies and novel list structure

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.

Generalized maximal utility for mining high average-utility itemsets

Generalized maximal utility for mining high average-utility itemsets

Dynamic maintenance model for high average-utility pattern mining with deletion operation

The high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

High average-utility itemset mining with multiple minimum utility threshold: A generalized approach

High Average-Utility Itemset (HAUI) mining is an emerging pattern mining technique to extract meaningful patterns from a transaction dataset. In the past, several HAUI mining algorithms have been developed with efficient upper-bounds and pruning strategies. However, all these algorithms use a single value of the minimum average-utility threshold for all the itemsets, which limits their applicability to real-life datasets. In order to address this issue, several HAUI mining algorithms with multiple average-utility thresholds have been developed that process the items in ascending order of their minimum average-utility threshold. However, it makes them inapplicable on traditional HAUI mining algorithms. Moreover, the perturbation in preference of items may reduce the performance of the algorithms. This paper presents an HAUI mining algorithm named Generalized High Average-utility Itemset Miner (GHAIM) that processes the items in ascending order of their Average Utility Upper-Bound (AUUB) like the traditional HAUI mining algorithms. A new approach named suffix minimum average-utility is proposed to retain the downward closure property of AUUB and several pruning methods. Besides, a compact list structure is also proposed to mine the HAUIs in one phase. Several pruning methods have been introduced for reducing search space and improving efficiency. Extensive experiments were performed with different sparse and dense types of datasets to determine GHAIM efficiency compared to two existing algorithms. It was observed from the results that GHAIM outperforms both the current algorithms in run time, memory consumption, number of candidate itemsets, and scalability.

Incrementally updating the high average-utility patterns with pre-large concept

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Technological evolution of wind energy with social network analysis

Purpose The purpose of this paper is to analyze the wind energy technologies using the social network analysis based on patent information. Analysis of patent documents with social network analysis is used to identify the most influential and connected technologies in the field of wind energy. Design/methodology/approach In the literature, patent data are often used to evaluate technologies. Patents related to wind energy technologies are obtained from the United States Patent and Trademark Office database and the relationships among sub-technologies based on Corporate Patent Classification (CPC) codes are analyzed in this study. The results of two-phase algorithm for mining high average-utility itemsets algorithm, which is one of the utility mining algorithm in data mining, is used to find associations among wind energy technologies for social network analysis. Findings The results of this study show that it is very important to focus on wind motors and technologies related to energy conversion or management systems reducing greenhouse gas emissions. The results of this study imply that Y02E, F03D and F05B CPC codes are the most influential CPC codes based on social network analysis. Originality/value Analysis of patent documents with social network analysis for technology evaluation is extremely limited in the literature. There is no research related to the analysis of patent documents with social network analysis, in particular CPC codes, for wind energy technology. This paper fills this gap in the literature. This study explores technologies related to wind energy technologies and identifies the most influential wind energy technologies in practice. This study also extracts useful information and knowledge to identify core corporate patent class (es) in the field of wind energy technology.

A fast high average-utility itemset mining with efficient tighter upper bounds and novel list structure

High-utility itemset mining is a prominent data-mining technique where the profit or weight of itemsets plays a crucial role in defining meaningful patterns. High average-utility itemset (HAUI) mining is an advancement over high-utility itemset mining, which introduces an unbiased measure called average utility to associate the utility of itemsets with their length. Several existing HAUI mining algorithms use various upper bounds such as average-utility upper bound, revised tighter upper bound, and looser upper bound to preserve pruning methods. However, these upper bounds overestimate the average-utility of itemsets and slow down the mining process. This paper presents a fast high average-utility itemset miner (FHAIM) algorithm, which uses two improved upper bounds and several efficient pruning strategies to avoid the processing of unpromising candidate itemsets. Moreover, a novel list structure named recommended average-utility list (RAUL) is presented to store the average-utility and the required information for pruning. The RAUL for an itemset can be constructed by joining the RAULs of its subsets to avoid excessive database scans. We have performed substantial experiments on various benchmark datasets to evaluate the performance of the FHAIM in comparison with two existing HAUI mining algorithms. Experimental results show that FHAIM outperforms the existing HAUI mining algorithms in terms of runtime, memory usage, join counts, and scalability.

Incrementally Updating the Discovered High Average-Utility Patterns With the Pre-Large Concept

High average-utility itemset mining (HAUIM) is an extension of high-utility itemset mining (HUIM), which provides a reliable measure to reveal utility patterns by considering the length of the mined pattern. Some research has been conducted to improve the efficiency of mining by designing a variety of pruning strategies and effective frameworks, but few works have focused on the maintenance algorithms in the dynamic environment. Unfortunately, most existing works of HAUIM still have to rescan databases multiple times when it is necessary. In this paper, the pre-large concept is used to update the discovered HAUIs in the newly inserted transactions and reduce the time of the rescanning process. To further improve the performance of the developed algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIM. Experiments were performed to compare the previous Apriori-like method and the proposed APHAUP algorithm with the two new upper-bounds in terms of the number of maintenance patterns and runtime in several datasets. The experimental results show that the proposed APHAUP algorithm has excellent performance and good potential to be applied in real applications.

EHAUSM: An efficient algorithm for high average utility sequence mining

Identifying high utility sequences in a quantitative sequence database is an important data mining task. However, a key problem of current approaches is that extensions of a high utility sequence often have a high utility. Hence, traditional techniques are often biased toward finding long patterns. To circumvent this problem, this paper proposes techniques for the problem of high average-utility sequence (HAUS) mining (HAUSM). HAUSs are more meaningful than high utility sequences because the former are found using the average-utility measure, which consider the length of patterns in utility calculations. HAUSM is more general than high average-utility itemset mining but it is also a more difficult problem because the downward-closure property used for search space reduction does not hold for the average-utility. To overcome that challenge, this paper introduces two upper bounds and a weak upper bound on the average-utility measure, and four width pruning, depth pruning, reducing, and tightening strategies. These strategies are designed to eliminate candidate HAUSs to speed up HAUS mining. Based on these theoretical results, a novel algorithm named EHAUSM is proposed for HAUSM. Experiments on both real-life and synthetic quantitative sequence databases have confirmed its efficiency in terms of memory consumption and runtime.

Mining High-Average Utility Itemsets with Positive and Negative External Utilities

High-utility itemset mining (HUIM) is an emerging data mining topic. It aims to find the high-utility itemsets by considering both the internal (i.e., quantity) and external (i.e., profit) utilities of items. High-average-utility itemset mining (HAUIM) is an extension of the HUIM, which provides a more fair measurement named average-utility, by taking into account the length of itemsets in addition to their utilities. In the literature, several algorithms have been introduced for mining high-average-utility itemsets (HAUIs). However, these algorithms assume that databases contain only positive utilities. For some real-world applications, on the other hand, databases may also contain negative utilities. In such databases, the proposed algorithms for HAUIM may not discover the complete set of HAUIs since they are designed for only positive utilities. In this study, to discover the correct and complete set of HAUIs with both positive and negative utilities, an algorithm named MHAUIPNU (mining high-average-utility itemsets with positive and negative utilities) is proposed. MHAUIPNU introduces an upper bound model, three pruning strategies, and a data structure. Experimental results show that MHAUIPNU is very efficient in reducing the size of the search space and thus in mining HAUIs with negative utilities.

A New Algorithm for High Average-utility Itemset Mining

High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and utility values tend to become higher for itemsets containing more items. Hence, HUIM algorithms discover a huge enormous number of long patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that selects patterns by considering both their utilities and lengths. In the last decades, several algorithms have been introduced to mine high average-utility itemsets. To speed up the HAUIM process, here a new algorithm is proposed which uses a new list structure and pruning strategy. Several experiments performed on real and synthetic datasets show that the proposed algorithm outperforms the state-of-the-art HAUIM algorithms in terms of runtime and memory consumption.

Efficient high average-utility itemset mining using novel vertical weak upper-bounds

Discovering high average utility itemsets (HAUIs) in a quantitative database is a popular data mining task, which aims at identifying sets of products (items) purchased together that have a high importance or yield a high profit. However, a key challenge of HAUI mining is that the downward-closure property (DCP) does not hold for the average-utility measure. Many studies have designed average-utility upper-bounds (UBs), which satisfy the DCP, to overestimate the average-utilities of itemsets. Several data structures and algorithms were proposed based on those UBs to discover all HAUIs. However, the proposed UBs are still loose, and the designed structures are still inefficient for computing UBs. To address this issue, this paper proposes four novel weak upper-bounds (WUBs) on the average-utility based on a vertical database representation, two new efficient pruning and tightening strategies to eliminate unpromising candidates early, and a novel list structure named NVUV-list to represent the search space and calculate the proposed WUBs quickly. The novel WUBs are tighter than well-known UBs and WUBs used in state-of-the-art algorithms such as MHAI, TUB-HAUPM and dHAUIM for mining HAUIs. Moreover, the NVUV-list structure is shown to be more efficient for computing vertical WUBs than the IDUL structure employed by dHAUIM. Based on the novel WUBs and NVUV-list structure, a novel algorithm named VMHAUI is proposed for HAUI mining. Experimental results show that it outperforms the state-of-the-art MHAI, TUB-HAUPM and dHAUIM algorithms by one to two orders of magnitudes, and that VMHAUI consumes less memory and performs much less join operations.

Mining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data

In the past years most of the research have been conducted on high average-utility itemset mining (HAUIM) with wide applications. However, most of the methods are used for centralized databases with a single machine performing the mining job. Existing algorithms cannot be applied for big data. We try to solve this issue, by developing a new method for mining high average-utility itemset mining in big data. Map Reduce also used in this paper. Many algorithms were proposed only mine HAUIs using a single minimum high average-utility threshold. In this paper we also try solve this by mining HAUIs multiple minimum high average-utility thresholds. We have developed two pruning methods namely Reduction of utility co-occurrence pruning Method (RUCPM) and Pruning without Scanning Database (PWSD).

Efficient Vertical Mining of High Average-Utility Itemsets Based on Novel Upper-Bounds

Mining High Average-Utility Itemsets (HAUIs) in a quantitative database is an extension of the traditional problem of frequent itemset mining, having several practical applications. Discovering HAUIs is more challenging than mining frequent itemsets using the traditional support model since the average-utilities of itemsets do not satisfy the downward-closure property. To design algorithms for mining HAUIs that reduce the search space of itemsets, prior studies have proposed various upper-bounds on the average-utilities of itemsets. However, these algorithms can generate a huge amount of unpromising HAUI candidates, which result in high memory consumption and long runtimes. To address this problem, this paper proposes four tight average-utility upper-bounds, based on a vertical database representation, and three efficient pruning strategies. Furthermore, a novel generic framework for comparing average-utility upper-bounds is presented. Based on these theoretical results, an efficient algorithm named dHAUIM is introduced for mining the complete set of HAUIs. dHAUIM represents the search space and quickly compute upper-bounds using a novel IDUL structure. Extensive experiments show that dHAUIM outperforms four state-of-the-art algorithms for mining HAUIs in terms of runtime on both real-life and synthetic databases. Moreover, results show that the proposed pruning strategies dramatically reduce the number of candidate HAUIs.

An Efficient Tree-Based Algorithm for Mining High Average-Utility Itemset

High-utility itemset mining (HUIM), which is an extension of well-known frequent itemset mining (FIM), has become a key topic in recent years. HUIM aims to find a complete set of itemsets having high utilities in a given dataset. High average-utility itemset mining (HAUIM) is a variation of traditional HUIM. HAUIM provides an alternative measurement named the average-utility to discover the itemsets by taking into consideration both of the utility values and lengths of itemsets. HAUIM is important for several application domains, such as, business applications, medical data analysis, mobile commerce, streaming data analysis, etc. In the literature, several algorithms have been proposed by introducing their own upper-bound models and data structures to discover high average utility itemsets (HAUIs) in a given database. However, they require long execution times and large memory consumption to handle the problem. To overcome these limitations, this paper, first, introduces four novel upper-bounds along with pruning strategies and two data structures. Then, it proposes a pattern growth approach called the HAUL-Growth algorithm for efficiently mining of HAUIs using the proposed upper-bounds and data structures. Experimental results show that the proposed HAUL-Growth algorithm significantly outperforms the state-of-the-art dHAUIM and TUB-HAUIM algorithms in terms of execution times, number of join operations, memory consumption, and scalability.

Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases

High-utility itemset mining (HUIM) is an extension of traditional frequent itemset mining, which considers both quantities and unit profits of items in a database to reveal highly profitable itemsets regardless of their size. High average-utility itemset mining (HAUIM) is designed to find average-utility itemsets by considering both their utility and the number of items that they contain. Thus, average-utility itemsets are obtained based on a fair utility measurement since the average utility typically does not increase much with the size of itemsets. However, most algorithms for discovering high average utility itemsets are designed to extract patterns from a static database. If the size of a database decreases or increases over time (e.g., as a result of transaction insertions), the database must be scanned again in batch mode to update the results. Thus, previously discovered knowledge is ignored and the time previously spent for pattern extraction is wasted. We thus present an incremental HAUIM algorithm for transaction insertion (FUP-HAUIMI) to maintain information about patterns when a database is updated, based on the FUP concept. An average-utility-list (AUL)-structure is first built by scanning the original database. Then, FUP-HAUIMI selects high average-utility upper-bound itemsets and categorizes them according to four cases. For each case, itemsets are maintained and updated using a specific updating procedure. While traversing the enumeration tree representing the search space in a depth-first way, a join operation is performed to quickly and incrementally update the AUL-structures. Several experiments were carried to evaluate the runtime, memory usage, number of potential patterns (candidates), and the scalability of the designed approach. Results show that the performance of FUP-HAUIMI is excellent compared to the state-of-the-art HAUI-Miner algorithm running in batch mode and the state-of-the-art incremental high-utility pattern mining (IHAUPM) algorithm for incremental average-utility pattern mining.