High Utility Itemset Mining Research Articles

Depth Impurity Pruned Strategies for Extracting High Utility Itemsets

Normally in a transaction database mining high utility itemsets indicates to the location of itemsets which is causing high utility like benefits. In spite of the fact that various important calculations have been proposed as of late, they bring about the issue of generating a huge amount of itemsets for mining to discover HUI. Mining is reduced by such an extended quantity as far as execution time and space complexity. When the database contains large amount of transactions, this condition may turn into mediocre. In this research paper, we account this concern by offering a state-of-the-art calculation named Depth Impurity Quality Index Pruned strategies which considers the complexity of sub-trees to more efficiently identify high-utility itemsets. It is an collection of common itemset which are used for mining and is significantly harder, inflexible. This is imputable to the absence of intrinsic organizational behaviour of HUI which could have worked. This paper suggests a high utility mining technique which make use of novel pruning approaches.The experimental outcomes disclose that the proposed method is exceptionally viable in killing unhopeful applicants in the database transactions.

Fast Identification of High Utility Itemsets from Candidates

High utility itemsets (HUIs) are sets of items with high utility, like profit, in a database. Efficient mining of high utility itemsets is an important problem in the data mining area. Many mining algorithms adopt a two-phase framework. They first generate a set of candidate itemsets by roughly overestimating the utilities of all itemsets in a database, and subsequently compute the exact utility of each candidate to identify HUIs. Therefore, the major costs in these algorithms come from candidate generation and utility computation. Previous works mainly focus on how to reduce the number of candidates, without dedicating much attention to utility computation, to the best of our knowledge. However, we find that, for a mining task, the time of utility computation in two-phase algorithms dominates the whole running time of these algorithms. Therefore, it is important to optimize utility computation. In this paper, we first give a basic algorithm for HUI identification, the core of which is a utility computation procedure. Subsequently, a novel candidate tree structure is proposed for storing candidate itemsets, and a candidate tree-based algorithm is developed for fast HUI identification, in which there is an efficient utility computation procedure. Extensive experimental results show that the candidate tree-based algorithm outperforms the basic algorithm and the performance of two-phase algorithms, integrating the candidate tree algorithm as their second step, can be significantly improved.

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.

Improved Market Basket Analysis with Utility Mining

Data Mining is a tool for retrieving novel and useful information contained in huge data stores. Conventional approaches to data mining techniques have majorly targeted the discovery of correlations among items that occur often in transactional databases. This method referred to as frequent item set mining believes that recurring item sets must be more significant to the user. Alternatively, in this paper, we attempt to simulate an algorithm for a recent development called Utility Mining, which studies the usefulness or utility of item sets, in addition to their frequency. This High Utility Item Set Mining facilitates the recognition of item sets having utility value greater than a lower limit specified by the seller. Though high confidence is attained, yet the limitation of our approach lies in low support. Consequentially, most useful frequent item sets might be ignored, leading to incorrect analysis of consumer behavior pattern. In the future, we intend to devise an ensemble algorithm which can tackle this issue.

A Review On High Utility Itemset Mining

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Maintenance algorithm for high average-utility itemsets with transaction deletion

High-utility itemset mining (HUIM) is an extension of traditional association-rule mining that can find profitable itemsets for decision-making. It faces, however, a limitation since the utility of an itemset increases along with the size of it. High-average utility itemset mining (HAUIM) provides a fair measure to find the average-utility of an itemset, which is more reasonable to design the sales strategies for making the efficient decision. Traditional algorithms of HAUIM mostly focus on mining high average-utility itemsets (HAUIs) from the static database. When the database size is changed, for example, transaction insertion/deletion, the discovered information is required to be updated, thus the updated database is necessary to be re-scanned for identifying the set of HAUIs in the batch manner. In this paper, we present an updating algorithm called FUP-HAUIMD to maintain the discovered HAUIs with transaction deletion. When some transactions in the database are deleted, the designed FUP-HAUIMD algorithm can easily update the discovered HAUIs without scanning the database all the time. The designed FUP-HAUIMD algorithm divides the itemsets into four cases based on the modified fast updated (MFUP) concept. The average-utility (AU)-list structure is further utilized to keep the necessary ramification for later mining progress. Experiments are then conducted to compare the designed FUP-HAUIMD algorithm with the state-of-the-art baseline algorithm running on the batch mode, and the developed approach shows better performance in terms of runtime, number of examined patterns, and scalability.

Efficiently updating the discovered high average-utility itemsets with transaction insertion

High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. This approach is, however, inappropriate in real-world applications since the utility of the itemset increases along with the number of items within it. High average-utility itemset mining (HAUIM) was designed to provide more reasonable utility measure by taking the size of the itemset into account. Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations. In this paper, an incremental high-average utility pattern mining (IHAUPM) algorithm is presented to handle the incremental database with transaction insertion. The well-known fast updated (FUP) concept in the FIM is modified to adopt the designed algorithm, thus efficiently updating the discovered HAUIs. Based on the designed model for HAUIM with transaction insertion, the proposed IHAUPM algorithm can easily only handle the inserted transactions. Experiments are carried on six datasets and the results showed that the designed algorithm has better performance than the state-of-the-art algorithms performing in the batch manner.

An efficient structure for fast mining 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.

An empirical evaluation of high utility itemset mining algorithms

High utility itemset mining (HUIM) has emerged as an important research topic in data mining, with applications to retail-market data analysis, stock market prediction, and recommender systems, etc. However, there are very few empirical studies that systematically compare the performance of state-of-the-art HUIM algorithms. In this paper, we present an experimental evaluation on 10 major HUIM algorithms, using 9 real world and 27 synthetic datasets to evaluate their performance. Our experiments show that EFIM and d2HUP are generally the top two performers in running time, while EFIM also consumes the least memory in most cases. In order to compare these two algorithms in depth, we use another 45 synthetic datasets with varying parameters so as to study the influence of the related parameters, in particular the number of transactions, the number of distinct items and average transaction length, on the running time and memory consumption of EFIM and d2HUP. In this work, we demonstrate that, d2HUP is more efficient than EFIM under low minimum utility values and with large sparse datasets, in terms of running time; although EFIM is the fastest in dense real datasets, it is among the slowest algorithms in sparse datasets. We suggest that, when a dataset is very sparse or the average transaction length is large, and running time is favoured over memory consumption, d2HUP should be chosen. Finally, we compare d2HUP and EFIM with two newest algorithms, mHUIMiner and ULB-Miner, and find these two algorithms have moderate performance. This work has reference value for researchers and practitioners when choosing the most appropriate HUIM algorithm for their specific applications.

A survey of incremental high‐utility itemset mining

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242This article is categorized under: Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation

Efficient mining of high utility itemsets with multiple minimum utility thresholds

Mining high utility itemsets is considered to be one of the important and challenging problems in the data mining literature. The problem offers greater flexibility to a decision maker in using item utilities such as profits and margins to mine interesting and actionable patterns from databases. Most of the current works in the literature, however, apply a single minimum utility threshold value and fail to consider disparities in item characteristics. This paper proposes an efficient method (MHUI) to mine high utility itemsets with multiple minimum utility threshold values. The presented method generates high utility itemsets in a single phase without an expensive intermediate candidate generation process. It introduces the concept of suffix minimum utility and presents generalized pruning strategies for efficiently mining high utility itemsets. The performance of the algorithm is evaluated against the state-of-the-art methods (HUI-MMU-TE and HIMU-EUCP) on eight benchmark datasets. The experimental results show that the proposed method delivers two to three orders of magnitude execution time improvement over the HUI-MMU-TE method. In addition, MHUI delivers one to two orders of magnitude execution time improvement over the HIMU-EUCP method, especially on moderately long and dense benchmark datasets. The memory requirements of the proposed algorithm was also found to be significantly lower.

TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns

High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit. HUIM, on the other hand, obtains the itemsets and is practical in commercial applications. A main challenge in HUIM is that HUIM should handle the exponential search space for HUIM when the number of distinct items and the size of the database are both too large. The other challenge is that existing HUIM methods overlook the length of high-utility itemsets; hence, a large itemset gets an unreasonable estimated profit as opposed to the actual value. Therefore, several algorithms were proposed to mine high average-utility itemsets. High average-utility itemset mining (HAUIM) is an extension for the traditional HUIM, which provides a different measure with HUIM. It discovers utility patterns by considering both their utilities and lengths. To reduce the searching space in HAUIM, average-utility upper-bound, looser upper-bound utility, and a revised tighter upper-bound model are proposed to prune the searching graph in HAUIM. These three upper-bounds for high average-utility itemsets decrease the number of candidate patterns efficiently. However, they still overestimate a high average-utility itemset and waste on assessing the unnecessary patterns. Two new tighter upper-bounds, maximum following utility upper-bound and top-k transaction-maximum utility upper-bound, are proposed in this paper to further contract the size of candidate pattern set. Experiments conducted on several benchmark data sets show that the proposed method outperforms the previous HAUIM algorithms in terms of runtime, the number of join operations, and scalability.

Selective Database Projections Based Approach for Mining High-Utility Itemsets

High-utility itemset mining (HilIM) is an emerging area of data mining and is widely used. HilIM differs from the frequent itemset mining (FIM), as the latter considers only the frequency factor, whereas the former has been designed to address both quantity and profit factors to reveal the most profitable products. The challenges of generating the HilI include exponential complexity in both time and space. Moreover, the pruning techniques of reducing the search space, which is available in FIM because of their monotonic and anti-monotonic properties, cannot be used in HilIM. In this paper, we propose a novel selective database projection-based HilI mining algorithm (SPHilI-Miner). We introduce an efficient data format, named HilI-RTPL, which is an optimum and compact representation of data requiring low memory. We also propose two novel data structures, viz, selective database projection utility list and Tail-Count list to prune the search space for HilI mining. Selective projections of the database reduce the scanning time of the database making our proposed approach more efficient. It creates unique data instances and new projections for data having less dimensions thereby resulting in faster HilI mining. We also prove upper bounds on the amount of memory consumed by these projections. Experimental comparisons on various benchmark data sets show that the SPHilI-Miner algorithm outperforms the state-of-the-art algorithms in terms of computation time, memory usage, scalability, and candidates generation.

Mining High Utility Itemsets Using Bio-Inspired Algorithms: A Diverse Optimal Value Framework

Mining high utility itemsets (HUI) is an interesting research problem in the field of data mining and knowledge discovery. Recently, bio-inspired computing has attracted considerable attention, leading to the development of new algorithms for mining HUIs. These algorithms have shown good performance in terms of efficiency, but are not guaranteed to find all HUIs in a database. That is, the quality is comparatively poor in terms of the number of discovered HUIs. To solve this problem, a new framework based on bio-inspired algorithms is proposed. This approach adjusts the standard roadmap of bio-inspired algorithms by proportionally selecting discovered HUIs as the target values of the next population, rather than maintaining the current optimal values in the next population. Thus, the diversity within populations can be improved. Three new algorithms based on the Bio-HUI framework are developed using the genetic algorithm, particle swarm optimization, and the bat algorithm, respectively. Extensive tests conducted on publicly available datasets show that the proposed algorithms outperform existing state-of-the-art algorithms in terms of efficiency, quality of results, and convergence speed.

High Utility Itemset Mining Using Partition Utility List Structure

High Utility Itemset Mining Using Partition Utility List Structure

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

High utility itemset (HUI) mining is emerging as an effective pattern mining technique, which discovers itemsets with their utility more than user defined utility threshold. Most of the HUI mining algorithms discover high utility patterns of all length (k-itemset), while many applications require the HUIs of certain lengths. FHM+ is a HUI mining algorithm which includes the length constraint and produces HUIs of the user defined lengths. FHM+ is suitable to run small size of data on standalone system. In the era of big data, the standalone system are not efficient to process huge transaction data. To accommodate this, a parallel version of FHM+ is proposed and named as P-FHM+ that process big transaction data in distributed manner on a multi-node cluster. Extensive experiments are conducted with proposed P-FHM+ on multiple real time datasets and observed that P-FHM+ outperforms FHM+ in terms or speed and scalability.

An Utility Based Approach for Business Intelligence to Discover Beneficial Itemsets With or Without Negative Profit in Retail Business Industry

Utility mining is defined as discovery of high utility itemsets from the large databases. It can be applied in business intelligence for business decision-making such as arranging products in shelf, catalogue design, customer segmentation, cross-selling etc. In this work a novel algorithm MAHUIM (matrix approach for high utility itemset mining) is proposed to reveal high utility itemsets from a transaction database. The proposed algorithm uses dynamic matrix structure. The algorithm scans the database only once and does not generate candidate itemsets. The algorithm calculates minimum threshold value automatically, without seeking from the user. The proposed algorithm is compared with the existing algorithms like HUI-Miner, D2HUP and EFIM. For handling negative utility values, MANHUIM algorithm is proposed and this is compared with HUINIV. For performance analysis, four benchmark datasets like Connect, Foodmart, Chess and Mushroom are used. The result shows that the proposed algorithms are efficient than the existing ones.

MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With Multiple Minimum Average-Utility Thresholds

High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous studies have presented algorithms to efficiently mine high average-utility itemsets (HAUIs). Most of these algorithms, however, only mine HAUIs using a single minimum high average-utility threshold, which limits their usefulness to analyze real data. This is a problem because different items are not equally important to the user. The importance of an item can be expressed for example in terms of weights, interestingness or unit profit. In the past, a baseline algorithm called HAUIM-MMAU was presented to mine HAUIs using multiple minimum high average-utility thresholds. However, it uses a generate-and-test approach to mine HAUIs using a level-wise approach, which is time consuming. In this paper, we propose an efficient algorithm to discover HAUIs based on the average-utility list structure. A tighter upper-bound model is used to reduce the search space instead of the one used in traditional HAUIM, which is called the auub model. Three pruning strategies are also respectively developed to increase the performance HAUIs. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime, memory usage, number of candidates, and scalability.

Efficiently mining high utility itemsets with negative unit profits

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method.

Review on high utility itemset mining algorithms for big data

This paper has been withdrawn.