C-HUIM

In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes one of the most emerging research topic in the field of data mining due to its wide range of applications in online e-commerce data analysis, identifying interesting patterns in biomedical data and for cross marketing solutions in retail business. It aims to discover the itemsets with high utilities efficiently by considering item quantities in a transaction and profit values of each item. However, it produces a tremendous number of HUIs, which imposes further burden in analysis of the extracted patterns and also degrades the performance of mining methods. Mining the set of closed + high utility itemsets (CHUIs) solves this issue as it is a loss-less and condensed representation of all HUIs. In this paper, we aim to present a new algorithm for finding CHUIs from a transactional database, called the CHUM (Closed + High Utility itemset Miner), which is scalable and efficient. The proposed mining algorithm adopts a tricky aimed vertical representation of the database in order to speed up the execution time in generating itemset closures and compute their utility information without accessing the database. The proposed method makes use of the item co-occurrences strategy in order to further reduce the number of intersections needed to be performed. Several experiments are conducted on various sparse and dense datasets and the simulation results clearly show the scalability and superior performance of our algorithm as compared to those for the existing state-of-the-art CHUD (Closed + High Utility itemset Discovery) algorithm.

Data mining identifies patterns in vast databases to support practical decision-making. Using association rule mining, the patterns in the transaction database are found and reveal client behavior. Frequent Itemset Mining (FIM) Finds Often-Buyd Items. FIM's disdain for item significance is a drawback. Practical applications depend on the relevance of the item. Thus, the High-Utility Itemset Mining (HUIM) challenge requires identifying the most profitable items in the transaction database. High-Utility Items (HUI) in the transaction database can be identified using several methods. HUIM methods based on utility lists are novel and outperform traditional methods in terms of memory consumption and execution time. The main limitation of this algorithm is the costly joins of the utility list. This research presents a highly effective swarm intelligence-based method for optimizing HUIM issues. The execution time of the suggested method is assessed. In addition, it is compared to advanced current methods. Testing on publicly available benchmark data sets shows that the swarm-based strategy outperforms current methods. This technique has wide applications in retail analytics, healthcare, fraud detection, finance, supply chain management, and recommendation systems, enabling businesses and researchers to optimize decision making and extract meaningful insights from transactional data.

Mining high utility itemsets is an important problem in datamining. This supplies more benefit itemsets than the traditional itemsets, but many high utility itemsets is not important because the poor relations among the items inside them. Recently, the theory of the high utility and interesting itemsets and the mining method have been proposed. A high utility and interesting itemset is a subset of a high utility itemset, and its relations are strong. In this paper, we introduce the KWU-Mining algorithm to mine the high utility and interesting itemsets based on the WIT tree structure. The experiment results of the KWU-Mining algorithm show that they are effective in many transaction databases.

High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web service, since items are actually different in many aspects in real applications. However, due to the lack of "downward closure property", the cost of candidate generation of high utility itemsets mining is intolerable in terms of time and memory space. This paper presents a Two-Phase algorithm which can efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. The performance of our algorithm is evaluated by applying it to synthetic databases and two real-world applications. It performs very efficiently in terms of speed and memory cost on large databases composed of short transactions, which are difficult for existing high utility itemsets mining algorithms to handle. Experiments on real-world applications demonstrate the significance of high utility itemsets in business decision-making, as well as the difference between frequent itemsets and high utility itemsets.

High utility itemset mining has emerged to be an important research issue in data mining since it has a wide range of real life applications. Although a number of algorithms have been proposed in recent years, the mining efficiency is still a big challenge since these algorithms suffer from either the problem of low efficiency of calculating candidates’ utilities or the problem of generating huge number of candidates. In this paper, we propose a novel data structure named PUN-list (PU-tree-Node list), which maintains both the utility information about an itemset and utility upper bound for facilitating the processing of mining high utility itemsets. Based on PUN-lists, we present a method, named MIP (Mining high utility Itemset using PUN-Lists), for efficiently mining high utility itemsets. The efficiency of MIP is achieved with three techniques. First, itemsets are represented by a highly condensed data structure, named PUN-list, which avoids costly and repeated utility computation. Second, the utility of an itemset can be efficiently calculated by scanning the PUN-list of the itemset and the PUN-lists of long itemsets can be efficiently constructed by the PUN-lists of short itemsets. Third, by employing the utility upper bound lying in the PUN-lists as the pruning strategy, MIP directly discovers high utility itemsets from the search space, named set-enumeration tree, without generating numerous candidates. Extensive experiments on various synthetic and real datasets show that MIP is very efficient since it is much faster than HUI-Miner, d2HUP, and UP-Growth + , especially on dense datasets.

Mining high-utility itemsets (HUIs) from a transaction database refers to the discovery of itemsets with high utilities like profits. Most of existing studies discover HUIs from a transaction database in two phases. In phase 1, different overestimation methods are applied to calculate the upper bounds of the utilities of itemsets. Since the overestimated utilities of itemsets are adopted, the itemsets whose overestimated utilities are no less than a user-specified threshold are selected as candidate HUIs, and they are verified by scanning the database one more time in phase 2. However, a large number of candidate HUIs incur two problems: 1) it requires excessive memory to store these candidates; 2) it needs a large amount of running time to calculate their exact utilities. Vertical data format has been applied to mine HUIs recently. However this kind of method cannot deal with transactions with the same items effectively so that the size of database cannot be reduced sufficiently. The overall performance of algorithms is degraded consequently. Thus an algorithm HUITWU is proposed in this paper for mining HUIs. A novel data structure HUITWU-Tree is adopted to efficiently calculate the utilities of itemsets in a database. Extensive studies with both sparse and dense datasets have demonstrated that our proposed algorithm is more than an order of magnitude faster and consumes less memory than the state-of-the-art algorithms.

P-FHM+: Parallel high utility itemset mining algorithm for big data processing

The discovery of high utility itemsets (HUIs) is an attractive topic in data mining. Because of its high computational cost, using heuristic methods is a promising approach to rapidly discovering sufficient HUIs. The artificial fish swarm algorithm is a heuristic method with many applications. Except the current position, artificial fish do not record additional previous information, as other related methods do. This is consistent with the HUI mining problem: that the results are not always distributed around a few extreme points. Thus, we study HUI mining from the perspective of the artificial fish swarm algorithm, and propose an HUI mining algorithm called HUIM-AF. We model the HUI mining problem with three behaviors of artificial fish: follow, swarm, and prey. We explain the HUIM-AF algorithm and compare it with two related algorithms on four publicly available datasets. The experimental results show that HUIM-AF can discover more HUIs than the existing algorithms, with comparable efficiency.

In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed; the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance.

Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset (HUI) that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation (PUC) algorithm has been proposed with novel grouping and load balancing strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility (TWU) values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup.

High-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and external utility values, which correspond, respectively, to the quantity sold of each item in each transaction and profit units. Therefore, internal and external utilities have symmetric effects on deciding whether an itemset is high-utility. The symmetric contributions of both utilities cause two major related challenges. First, itemsets with low external utility values can easily exceed the minimum utility threshold if they are sold extensively. In this case, such itemsets can be found more efficiently using frequent itemset mining. Second, a large number of high-utility itemsets are generated, which can result in interesting or important high-utility itemsets that are overlooked. This study presents an asymmetric approach in which the internal utility values are ignored when finding high-utility itemsets with high external utility values. The experimental results of two real datasets reveal that the external utility values have fundamental effects on the high-utility itemsets. The results of this study also show that this effect tends to increase for high values of the minimum utility threshold. Moreover, the proposed approach reduces the execution time.

High utility itemset mining is an important extension of frequent itemset mining which considers unit profits and quantities of items as external and internal utilities, respectively. Since the utility function has not downward closure property, an overestimated value of utility is obtained using an anti-monotonic upper bound of utility function to prune the search space and improve the efficiency of high utility itemset mining methods. Transaction-weighted utilization (TWU) of itemset was the first and one of the most important functions which has been used as the anti-monotonic upper bound of utility by various algorithms. A variety of high utility itemset mining methods have attempted to tighten the utility upper bound and have exploited appropriate pruning strategies to improve mining efficiency. Although TWU and its improved alternatives have attempted to increase the efficiency of high utility itemset mining methods by pruning their search spaces, they suffer from a significant number of generated candidates which are high-TWU but are not high utility itemsets. Calculating the actual utilities of low utility candidates needs to multiple scanning of the dataset and thus imposes a huge overhead to the mining methods, which can cause to lose the pruning benefits of the upper bounds. Proposing appropriate pruning strategies, exploiting efficient data structures, and using tight anti-monotonic upper bounds can overcome this problem and lead to significant performance improvement in high utility itemset mining methods. In this paper, a new projection-based method, called MAHI (matrix-aided high utility itemset mining), is introduced which uses a novel utility matrix-based pruning strategy, called MA-prune to improve the high utility itemset mining performance in terms of execution time. The experimental results show that MAHI is faster than former algorithms.

Discovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find profitable itemsets that are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM$_{all\text{-}confidence}$ and FCHM$_{bond}$, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs.

Mining summarization of high utility itemsets

Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.