Mining High Utility Item Sets Through a Swarm-Based Optimization Method

Mining high-utility itemsets based on particle swarm optimization

In this internet world, most of the transactions are done in online especially in ecommerce field which creates enormous size of transactional database that grows dynamically at each second. Really it is tough job to analyses the growing transactional database for the betterment of ecommerce business growth. Determining the Interesting pattern form incremental transactional database is evolving research area in the field of Data mining. Frequent Itemset Mining (FIM) approach is followed in earlier days to find the frequent pattern from transactional database. FIM has significant drawback of omitting the interesting factor about each items, such as quantity, price, profit and etc. This drawback is addressed in High Utility Itemset Mining (HUIM) which considers interesting factors of each item. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.

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.

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.

High-utility itemset mining is one of the highly researched area. Many research enthusiasts have discovered various techniques and algorithms to mine high-utility itemsets from transaction databases. One of the limitations of the existing high-utility itemset mining techniques is that there is no any generalized framework for applying the custom combinations of input parameters and any other constraints for mining high utility itemsets. This paper proposes a novel customizable framework to discover customized high utility itemsets (C-HUI). Users can customize the constraints and/or input parameters as per their requirements. A novel C-HUIM algorithm is used to discover customized high utility itemsets (C-HUI) from real-life datasets. The experimental results of the proposed framework and C-HUIM algorithm highlight the effectiveness of the approach.

Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining

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

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.

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.

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

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.

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 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.

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.