High Utility Itemset Mining Research Articles

A Survey on High Utility Itemsets Mining

A Survey on High Utility Itemsets Mining

Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.

Efficient high utility itemset mining using buffered utility-lists

Discovering high utility itemsets in transaction databases is a key task for studying the behavior of customers. It consists of finding groups of items bought together that yield a high profit. Several algorithms have been proposed to mine high utility itemsets using various approaches and more or less complex data structures. Among existing algorithms, one-phase algorithms employing the utility-list structure have shown to be the most efficient. In recent years, the simplicity of the utility-list structure has led to the development of numerous utility-list based algorithms for various tasks related to utility mining. However, a major limitation of utility-list based algorithms is that creating and maintaining utility-lists are time consuming and can consume a huge amount of memory. The reasons are that numerous utility lists are built and that the utility-list intersection/join operation to construct a utility-list is costly. This paper addresses this issue by proposing an improved utility-list structure called utility-list buffer to reduce the memory consumption and speed up the join operation. This structure is integrated into a novel algorithm named ULB-Miner (Utility-List Buffer for high utility itemset Miner), which introduces several new ideas to more efficiently discover high utility itemsets. ULB-Miner uses the designed utility-list buffer structure to efficiently store and retrieve utility-lists, and reuse memory during the mining process. Moreover, the paper also introduces a linear time method for constructing utility-list segments in a utility-list buffer. An extensive experimental study on various datasets shows that the proposed algorithm relying on the novel utility-list buffer structure is highly efficient in terms of both execution time and memory consumption. The ULB-Miner algorithm is up to 10 times faster than the FHM and HUI-Miner algorithms and consumes up to 6 times less memory. Moreover, it performs well on both dense and sparse datasets.

Parallel Mining for High Utility Itemsets Mining by Efficient Data Structure

Mining high utility itemsets in transaction database is an important task in data mining and widely applied in many areas. Recently, many algorithms have been proposed, but most algorithms for identifying high utility itemsets need to generate candidate sets by overestimating their utility and then calculating their exact utility value. Therefore, the number of candidate itemsets is much larger than the actual number of high utility itemsets. In this paper, we introduce the Retail Transaction-Weighted Utility (RTWU) structure and propose two algorithms: EAHUIMiner algorithm and PEAHUI-Miner parallel algorithm. They have been experimented and compared to the two most efficient algorithms: EFIM and FHM. Results show that our algorithm is better with sparse datasets. DOI: 10.32913/rd-ict.vol3.no14.519

HMiner: Efficiently mining high utility itemsets

Abstract High utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets. This paper advances the state-of-the-art and presents HMiner, a high utility itemset mining method. HMiner utilizes a few novel ideas and presents a compact utility list and virtual hyperlink data structure for storing itemset information. It also makes use of several pruning strategies for efficiently mining high utility itemsets. The proposed ideas were evaluated on a set of benchmark sparse and dense datasets. The execution time improvements ranged from a modest thirty percent to three orders of magnitude across several benchmark datasets. The memory consumption requirements also showed up to an order of magnitude improvement over the state-of-the-art methods. In general, HMiner was found to work well in the dense regions of both sparse and dense benchmark datasets.

Mining top-k high-utility itemsets from a data stream under sliding window model

High-utility itemset mining has gained significant attention in the past few years. It aims to find sets of items i.e. itemsets from a database with utility no less than a user defined threshold. The notion of utility provides more flexibility to an analyst to mine relevant itemsets. Nowadays, a continuous and unbounded stream of data is generated from web-clicks, transaction flow from retail stores, sensor networks, etc. Mining high-utility itemsets from a data stream is a challenging task as the incoming stream of data has to be processed on the fly with time and storage memory constraints. The number of high-utility itemsets depends on the user-defined threshold. A large number of itemsets can be generated at very low threshold values and vice versa. It can be a tedious task to set a threshold value to get a reasonable number of itemsets. Top-k high-utility itemset mining was coined to address this issue. k is the number of high-utility itemsets in the result set as defined by the user. In this paper, we propose a data structure and an efficient algorithm for mining top-k high-utility itemsets from a data stream. The algorithm has a single phase that does not generate any candidates, unlike many algorithms that work in two phases, i.e., candidate generation followed by candidates verification. We conduct extensive experiments on several real and synthetic datasets. Experimental results demonstrate that our proposed algorithm performs 20 to 80 times better on sparse datasets and 300 to 700 times on dense datasets than the state-of-the-art algorithm in terms of computation time. Furthermore, our proposed algorithm requires less memory compared to the state-of-the-art algorithm.

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

A two-phase approach to mine short-period high-utility itemsets in transactional databases

The discovery of high-utility itemsets (HUIs) in transactional databases has attracted much interest from researchers in recent years since it can uncover hidden information that is useful for decision making, and it is widely used in many domains. Nonetheless, traditional methods for high-utility itemset mining (HUIM) utilize the utility measure as sole criterion to determine which item/sets should be presented to the user. These methods ignore the timestamps of transactions and do not consider the period constraint. Hence, these algorithms often finds HUIs that are profitable but that seldom occur in transactions. In this paper, we address this limitation of previous methods by pushing the period constraint in the HUI mining process. A new framework called short-period high-utility itemset mining (SPHUIM) is designed to identify patterns in a transactional database that appear regularly, are profitable, and also yield a high utility under the period constraint. The aim of discovering short-period high-utility itemsets (SPHUI) is hence to identify patterns that are interesting both in terms of period and utility. The paper proposes a baseline two-phase short-period high-utility itemset (SPHUITP) mining algorithm to mine SPHUIs in a level-wise manner. Then, to reduce the search space of the SPHUITP algorithm and speed up the discovery of SPHUIs, two pruning strategies are developed and integrated in the baseline algorithm. The resulting algorithms are denoted as SPHUIMT and SPHUITID, respectively. Substantial experiments both on real-life and synthetic datasets show that the three proposed algorithms can efficiently and effectively discover the complete set of SPHUIs, and that considering the short-period constraint and the utility measure can greatly reduce the number of patterns found.

A hybrid framework for mining high-utility itemsets in a sparse transaction database

High-utility itemset mining aims to find the set of items with utility no less than a user-defined threshold in a transaction database. High-utility itemset mining is an emerging research area in the field of data mining and has important applications in inventory management, query recommendation, systems operation research, bio-medical analysis, etc. Currently, known algorithms for this problem can be classified as either 1-phase or 2-phase algorithms. The 2-phase algorithms typically consist of tree-based algorithms which generate candidate high-utility itemsets and verify them later. A tree data structure generates candidate high-utility itemsets quickly by storing some upper bound utility estimate at each node. The 1-phase algorithms typically consist of inverted-list based and transaction projection based algorithms which avoid the generation of candidate high-utility itemsets. The inverted list and transaction projection allows computation of exact utility estimates. We propose a novel hybrid framework that combines a tree-based and an inverted-list based algorithm to efficiently mine high-utility itemsets. Algorithms based on the framework can harness benefits of both types of algorithms. We report experiment results on real and synthetic datasets to demonstrate the effectiveness of our framework.

Phương pháp song song khai phá tập lợi ích cao dựa trên chỉ số hình chiếu

High utility itemsets (HUIs) mining is one of popular problems in data mining. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. All the parallel algorithms to try reduce synchronization cost and caculation global profit of itemsets. In this paper, we present a parallel method for mining HUIs from projection-based indexing to speed up performance and reduce memory requirements. The experimental results show that the performance and number candidate of our algorithm is better than some non parallel algorithms.

A Proposed Method to Select Potential Item Set for High Utility Item Set Mining using Genetic Algorithm Techniques

A Proposed Method to Select Potential Item Set for High Utility Item Set Mining using Genetic Algorithm Techniques

A fast algorithm for mining high average-utility itemsets

Mining high-utility itemsets (HUIs) in transactional databases has become a very popular research topic in recent years. A popular variation of the problem of HUI mining is to discover high average-utility itemsets (HAUIs), where an alternative measure called the average-utility is used to evaluate the utility of itemsets by considering their lengths. Albeit, HAUI mining has been studied extensively, current algorithms often consume a large amount of memory and have long execution times, due to the large search space and the usage of loose upper bounds to estimate the average-utilities of itemsets. In this paper, we present a more efficient algorithm for HAUI mining, which includes three pruning strategies to provide a tighter upper bound on the average-utilities of itemsets, and thus reduce the search space more effectively to decrease the runtime. The first pruning strategy utilizes relationships between item pairs to reduce the search space for itemsets containing three or more items. The second pruning strategy provides a tighter upper bound on the average-utilities of itemsets to prune unpromising candidates early. The third strategy reduces the time for constructing the average-utility-list structures for itemsets, which is used to calculate their upper bounds. Substantial experiments conducted on both real-life and synthetic datasets show that the proposed algorithm with three pruning strategies can efficiently and effectively reduce the search space for mining HAUIs, when compared to the state-of-the-art algorithms, in terms of runtime, number of candidates, memory usage, performance of the pruning strategies and scalability.

An efficient algorithm for mining top-k on-shelf high utility itemsets

High on-shelf utility itemset (HOU) mining is an emerging data mining task which consists of discovering sets of items generating a high profit in transaction databases. The task of HOU mining is more difficult than traditional high utility itemset (HUI) mining, because it also considers the shelf time of items, and items having negative unit profits. HOU mining can be used to discover more useful and interesting patterns in real-life applications than traditional HUI mining. Several algorithms have been proposed for this task. However, a major drawback of these algorithms is that it is difficult for users to find a suitable value for the minimum utility threshold parameter. If the threshold is set too high, not enough patterns are found. And if the threshold is set too low, too many patterns will be found and the algorithm may use an excessive amount of time and memory. To address this issue, we propose to address the problem of top-k on-shelf high utility itemset mining, where the user directly specifies k, the desired number of patterns to be output instead of specifying a minimum utility threshold value. An efficient algorithm named KOSHU (fast top-K on-shelf high utility itemset miner) is proposed to mine the top-k HOUs efficiently, while considering on-shelf time periods of items, and items having positive and/or negative unit profits. KOSHU introduces three novel strategies, named efficient estimated co-occurrence maximum period rate pruning, period utility pruning and concurrence existing of a pair 2-itemset pruning to reduce the search space. KOSHU also incorporates several novel optimizations and a faster method for constructing utility-lists. An extensive performance study on real-life and synthetic datasets shows that the proposed algorithm is efficient both in terms of runtime and memory consumption and has excellent scalability.

A Survey on Mining High Utility Itemsets from Transactional Databases

A Survey on Mining High Utility Itemsets from Transactional Databases

PHUIMUS: A Potential High Utility Itemsets Mining Algorithm Based on Stream Data with Uncertainty

High utility itemsets (HUIs) mining has been a hot topic recently, which can be used to mine the profitable itemsets by considering both the quantity and profit factors. Up to now, researches on HUIs mining over uncertain datasets and data stream had been studied respectively. However, to the best of our knowledge, the issue of HUIs mining over uncertain data stream is seldom studied. In this paper, PHUIMUS (potential high utility itemsets mining over uncertain data stream) algorithm is proposed to mine potential high utility itemsets (PHUIs) that represent the itemsets with high utilities and high existential probabilities over uncertain data stream based on sliding windows. To realize the algorithm, potential utility list over uncertain data stream (PUS-list) is designed to mine PHUIs without rescanning the analyzed uncertain data stream. And transaction weighted probability and utility tree (TWPUS-tree) over uncertain data stream is also designed to decrease the number of candidate itemsets generated by the PHUIMUS algorithm. Substantial experiments are conducted in terms of run-time, number of discovered PHUIs, memory consumption, and scalability on real-life and synthetic databases. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful PHUIs from uncertain data streams.

Rup/Frup-growth: An Efficient Algorithm for Mining High Utility Itemsets

High Utility Itemsets Mining is a topic in the field of data mining. Many algorithms have been proposed in recent years, but they generate a large number of candidate itemsets for high utility itemsets. So the bottleneck of these algorithms is the processing of the candidates; the situation may become worse with the increasing of the number of long transaction itemsets or the decreasing of the minimum utility threshold. Meanwhile, in real-life marketing, the profit value of an item should also be considered along with its selling frequency and quantity. To address these issues, proposes an improvement of the UP-Growth algorithm called RUP-Growth, and develops a new algorithm called FRUP-Growth to take into consideration both the minimum support number and the minimum utility value to mine frequent & high utility itemsets. The experimental results show that our proposed strategies are more efficient and effective; especially with real-life marketing database, the advantage is more obvious.

EHAUPM: Efficient High Average-Utility Pattern Mining With Tighter Upper Bounds

High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that provides an alternative measure, called the average utility, to select patterns by considering both their utilities and lengths. In the last decades, several algorithms have been developed to mine high average-utility itemsets (HAUIs). But most of them consume large amounts of memory and have long execution times, since they generally utilize the average-utility upper-bound ( auub ) model to overestimate the average utilities of itemsets. To improve the performance of HAUIM, this paper proposes two novel tighter upper-bound models as alternative to the traditional auub model for miningHAUIs. The looser upper-bound model considers the remaining-maximum utility in transactions to reduce the upper bound on the utilities of itemsets. The second upper-bound model ignores irrelevant items in transactions to further tighten the upper bound. Three pruning strategies are also designed to reduce the search space for mining HAUIs by a greater amount compared with the state-of-the-art HAUI-Miner algorithm. Experiments conducted on several benchmark data sets show that the designed algorithm integrating the two novel upper-bound models outperforms the traditional HAUI-Miner algorithm in terms of runtime, memory usage, number of join operations, and scalability.

A Better Approach for Mining High Utility Itemset from Transactional Databases

A Better Approach for Mining High Utility Itemset from Transactional Databases

Reduced Overestimated Utility and Pruning Candidates using Incremental Mining

Objectives: We proposed a Maximum Utility Growth (MUG) algorithm and Maximum Item Quantity (MIQ) Tree structure is used to decrease the number of candidates and reduce the candidate Itemset. Methods: In high utility Itemset mining profit and quantity of items are important in transaction. Several algorithms have proposed to solve the issues of large number of candidate Itemset generation. The proposed MUG algorithm and MIQ-Tree structure is used to reduce candidate Itemset and overestimated utilities in incremental mining. Moreover, the MUG with second strategy of approach calculates maximum utility of each expanded Itemset from the current prefix with supports in mining process. Finding: MUG algorithm and MIQ-Tree are proposed for mining high utility itemset to reduce the overestimated utility and it proposed two strategies for pruning candidate item sets efficiently in the process. It reduces the number of candidate and improves the performance of incremental mining. MIQ-Tree proposed to construct the tree with single-pass. The tree structure can restricted without new database and it employs decreasing the overestimated utility. MUG proposed to prune the number of candidate itemset with two strategies (I) Pruning 1-Itemset Candidates with Real Item Utilities and (II) Pruning Candidates with Estimated Maximum Itemset Utility. The experimental shows that the method developed the performance by decreasing number of candidate itemset. Performance evaluation shows that it reduces the number of candidate and its runtime with equal usage memory. Through the strategy, it can effectively eliminate the search space in the process. Applications: There is large amount of real world application such as retail marketing and stock marketing has emerged techniques high utility Itemset mining.

An ACO-based approach to mine high-utility itemsets

High-utility itemset mining (HUIM) is a major contemporary data mining issue. It is different from frequent itemset mining (FIM), which only considers the frequency factor. HUIM applies both the quantity and profit factors to be used to reveal the most profitable products. Several previous approaches have been proposed to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of the database are both very large. Therefore, two evolutionary computation (EC) techniques, genetic algorithm (GA) and particle swarm optimization (PSO), were previously proposed to mine HUIs. In these studies, GAs and PSOs also could obtain the huge amount of high-utility items in a limitation time. In this paper, a novel algorithm based on the other evolutionary computation technique, ant colony optimization (ACO), is proposed to resolve this issue. Unlike GAs and PSOs, ACOs produce a feasible solution in a constructive way. They can avoid generating unreasonable solutions as much as possible. Thus, a well-defined ACO approach can always obtain suitable solutions efficiently. An ant colony system (ACS), which is extended from ACO and consists of high-utility itemset mining by ACS (HUIM-ACS), is proposed to efficiently find HUIs. In general, an EC algorithm cannot make sure the provided solution is the global optimal solution. But the designed HUIM-ACS algorithm maps the completed solution space into the routing graph and includes two pruning processes. Therefore, it guarantees that it obtains all of the HUIs when there is no candidate edge from the starting point. In addition, HUIM-ACS does not estimate the same feasible solution again in its process in order to avoid wasting computational resource. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of the number of discovered HUIs, and convergence.