High Utility Itemsets Research Articles

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.

Top-k high utility itemset mining: current status and future directions

Abstract High utility itemsets mining (HUIM) is an important sub-field of frequent itemset mining (FIM). Recently, HUIM has received much attention in the field of data mining. High utility itemsets (HUIs) have proven to be quite useful in marketing, retail marketing, cross-marketing, and e-commerce. Traditional HUIM approaches suffer from a drawback as they need a user-defined minimum utility ( $ min\_util $ ) threshold. It is not easy for the users to set the appropriate $ min\_util $ threshold to find actionable HUIs. To target this drawback, top-k HUIM has been introduced. top-k HUIM is more suitable for supermarket managers and retailers to prepare appropriate strategies to generate higher profit. In this paper, we provide an in-depth survey of the current status of top-k HUIM approaches. The paper presents the task of top-k HUIM and its relevant definitions. It reviews the top-k HUIM approaches and presents their advantages and disadvantages. The paper also discusses the important strategies of the top-k HUIM, their variations, and research opportunities. The paper provides a detailed summary, analysis, and future directions of the top-k HUIM field.

Deep neural learned bipolar sigmoid association rule mining for discovering high frequent and utility itemsets

Deep neural learned bipolar sigmoid association rule mining for discovering high frequent and utility itemsets

Notice of Retraction: CHN: an efficient algorithm for mining closed high utility itemsets with negative utility

Retracted.

Mining closed high utility itemsets using sliding window infrastructure model over data stream

Mining closed high utility itemsets using sliding window infrastructure model over data stream

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

New approaches for mining high utility itemsets with multiple utility thresholds

New approaches for mining high utility itemsets with multiple utility thresholds

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.

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.

Efficient mining of concise and informative representations of frequent high utility itemsets

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named wlbu on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on wlbu. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness.

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

Targeted High-Utility Itemset Querying

Targeted High-Utility Itemset Querying