High Utility Itemset Mining Research Articles

High utility itemset mining using path encoding and constrained subset generation

In this paper a two phase approach for high utility itemset mining has been proposed. In the first phase potential high utility itemsets are generated using potential high utility maximal supersets. The transaction weighted utility measure is used in ascertaining the potential high utility itemsets. The maximal supersets are obtained from high utility paths ending in the items in the transaction database. The supersets are constructed without using any tree structures. The prefix information of an item in a transaction is stored in the form of binary codes. Thus, the prefix information of a path in a transaction is encoded as binary codes and stored in the node containing the item information. The potential high utility itemsets are generated from the maximal supersets using a modified set enumeration tree. The high utility itemsets are then obtained from the set enumeration tree by calculating the actual utility by scanning the transaction database. The experiments highlight the superior performance of the system compared to other similar systems in the literature.

Efficient top-k high utility itemset mining on massive data

In practical applications, top-k high utility itemset mining (top-k HUIM) is an interesting operation to find the k itemsets with the highest utilities. It is analyzed that, the existing algorithms only can deal with the small and medium-sized data, and their performance degrades significantly on massive data. This paper presents a novel top-k HUIM algorithm PTM which can mine top-k high utility itemsets on massive data efficiently. PTM maintains the transaction database as a set of prefix-based partitions, each of which can fit in the memory and keeps the transactions with the same prefix item. The utility of an itemset can be computed by the transactions in one particular partition. PTM processes the prefix-based partitions in the order of average transaction utility to raise utility threshold faster. By the concise assistant data structures, PTM can skip majority of the partitions directly. For the partitions to be processed, PTM utilizes depth-first search on the set enumeration tree of the promising items to find the required results. The full-suffix-utility-based subtree pruning rule is devised to reduce the exploration space of set enumeration tree effectively. The extensive experimental results show that PTM can discover the top-k high utility itemsets on massive data efficiently.

An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix

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: a Boolean operators-based modified grey wolf optimization algorithm

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.

Incrementally updating the high average-utility patterns with pre-large concept

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Empirical Methodology and Innovative Algorithm for Mining High Utility Itemset on Temporal Data

Ever since the humans found the significance of data we are using different methodologies to extricate data patterns in the most useful way to reach at various conclusions based on our needs. The proper utilization of data will leads to meaning full results. Here our focus is to expand the concept of "High utility itemset mining" and also introducing the concept of "timestamp" on it. Here we proposed an effective algorithm to find the best possible data patterns that generate maximum profit within the given "time period" and also proposed a new framework to calculate the "utility support”. Our analysis can be utilized to improve profitability in business and also make better decisions for improving sales in the organization.

A fast high average-utility itemset mining with efficient tighter upper bounds and novel list structure

High-utility itemset mining is a prominent data-mining technique where the profit or weight of itemsets plays a crucial role in defining meaningful patterns. High average-utility itemset (HAUI) mining is an advancement over high-utility itemset mining, which introduces an unbiased measure called average utility to associate the utility of itemsets with their length. Several existing HAUI mining algorithms use various upper bounds such as average-utility upper bound, revised tighter upper bound, and looser upper bound to preserve pruning methods. However, these upper bounds overestimate the average-utility of itemsets and slow down the mining process. This paper presents a fast high average-utility itemset miner (FHAIM) algorithm, which uses two improved upper bounds and several efficient pruning strategies to avoid the processing of unpromising candidate itemsets. Moreover, a novel list structure named recommended average-utility list (RAUL) is presented to store the average-utility and the required information for pruning. The RAUL for an itemset can be constructed by joining the RAULs of its subsets to avoid excessive database scans. We have performed substantial experiments on various benchmark datasets to evaluate the performance of the FHAIM in comparison with two existing HAUI mining algorithms. Experimental results show that FHAIM outperforms the existing HAUI mining algorithms in terms of runtime, memory usage, join counts, and scalability.

A general-purpose distributed pattern mining system

This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.

MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY

The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.

RETRACTED ARTICLE: An improved memory adaptive up-growth to mine high utility itemsets from large transaction databases

High utility itemset (HUI) mining identifies an interesting pattern from transactional databases by taking into consider the utility of each in the transaction for instance, margins or profits. Many candidates are generated for HUIs which reduces the mining performance. Frequent pattern-growth (FP-Growth) algorithm was widely used to discover the frequent itemsets using FP-Tree. But, UP-Tree was constructed to store high utility item set. The mining of UP-Tree by FP-Growth extracts high utility itemset and generates too many candidates. So, UP-Growth and UP-Growth+ was proposed to shorten the candidate itemsets. In UP-Growth, two tactics such as discarding local unpromising items (DLU) and decreasing local node (DLN) were used in FP-Growth. In UP-Growth+, two strategies such as DLU and its estimated node utilities (DNU) and DLN tools for the nodes of internal UP-Tree by evaluated the descendant nodes (DNN) were incorporated in FP-Growth. However, the UP-Growth and UP-Growth+ has the problem of poor spatial and temporal localities. Initially, the UP-Tree is created in available main memory then extended to secondary memory when the transaction is large. The storage of data structure in secondary memory and accessibility is not clearly derived in existing algorithms. In this paper, the spatial locality issues of UP-Growth and UP-Growth+ is solved by rearranging nodes of UP-Tree in a depth first manner and temporal locality issues of UP-Growth and UP-Growth+ is solved by page blocking technique which reorganizes the execution part of UP-Growth and UP-Growth+. The computational part is rearranged. In addition to, the memory management strategy is introduced to minimize the space requirement of high utility itemsets. Thus the proposed Improved Adaptive UP-Growth (IAUP-Growth) and Improved Adaptive UP-Growth+ (IAUP-Growth+) overcomes the spatial and temporal locality problem and effectively reduces the memory usage.

Mining correlated high-utility itemsets using various measures

AbstractDiscovering 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 Maximal High Utility Itemsets on Dynamic Profit Databases

To overcome the limitation of high-utility itemset mining, more compact, lossless, and concise representations of high utility itemsets (HUIs) have been proposed in previous works, such as closed HUIs (CHUIs) or maximal HUIs (MHUIs). Focusing into MHUI mining, in this article, we present efficient approaches to directly mine MHUIs from transactional databases without generating any candidates. The proposed algorithms, which all execute in one phase, utilize many efficient data structures and pruning techniques such as EUCP combined with EUCS, CUIP combined with FUCS, and the P-set structure to significantly reduce the search space and remove nonpromising itemsets, thus, increase the performance of the MHUI mining process. Furthermore, while previous works assumed that the unit profit of items is fixed, which is not practical in many real-world applications, our work resolved this issue by applying a new utility calculation into the mining process to reflect the true nature of real-world databases, thus, generating more accurate results.

Proof Learning in PVS With Utility Pattern Mining

Interactive theorem provers (ITPs) are software tools that allow human users to write and verify formal proofs. In recent years, an emerging research area in ITPs is proof mining, which consists of identifying interesting proof patterns that can be used to guide the interactive proof process in ITPs. In previous studies, some data mining techniques, such as frequent pattern mining, have been used to analyze proofs to find frequent proof steps. Though useful, such models ignore the facts that not all proof steps are equally important. To address this issue, this paper proposes a novel proof mining approach based on finding not only frequent patterns but also high utility patterns to find proof steps of high importance (utility). A proof process learning approach is proposed based on high utility itemset mining (HUIM) for the PVS (Prototype Verification System) proof assistant. Proofs in PVS theories are first abstracted to a computer-processable corpus, where each line represents a proof sequence and proof commands in proof sequences are associated with utilities representing their weightage (importance). HUIM techniques are then applied on the corpus to discover frequent proof steps/high utility patterns and their relationships with each other. Experimental results suggest that combining frequent pattern mining techniques, such as sequential pattern mining and high utility itemset mining, with proof assistants, such as PVS, is useful to learn and guide the proof development process.

Mining of top-k high utility itemsets with negative utility

High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.

An Efficient Method for Mining Closed Potential High-Utility Itemsets

High-utility itemset mining (HUIM) has become a key phase of the pattern mining process, which has wide applications, related to both quantities and profits of items. Many algorithms have been proposed to mine high-utility itemsets (HUIs). Since these algorithms often return a large number of discovered patterns, a more compact and lossless representation has been proposed. The recently proposed closed high utility itemset mining (CHUIM) algorithms were designed to work with certain types of databases (e.g., those without probabilities). In fact, real-world databases might contain items or itemsets associated with probability values. To effectively mine frequent patterns from uncertain databases, several techniques have been developed, but there does not exist any method for mining CHUIs from this type of databases. This work presents a novel and efficient method without generating candidates, named CPHUI-List, to mine closed potential high-utility itemsets (CPHUIs) from uncertain databases. The proposed algorithm is DFS-based and utilizes the downward closure property of high transaction-weighted probabilistic mining to prune non-CPHUIs. It can be seen from the experiment evaluations that the proposed algorithm has better execution time and memory usage than the CHUI-Miner.

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important property and thus are inapplicable or generate inaccurate results on real data. To address this issue, this paper proposes a novel algorithm named Multi-Core HUI Miner (MCH-Miner). It adapts techniques introduced in the iMEFIM algorithm to run on a parallel multi-core architecture to efficiently mine HUIs in dynamic transaction databases. An empirical evaluation shows that in most cases, MCH-Miner is significantly faster than iMEFIM, and that the cost of database scans is reduced.

Mining Correlated High Utility Itemsets in One Phase

High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage.

Incrementally Updating the Discovered High Average-Utility Patterns With the Pre-Large Concept

High average-utility itemset mining (HAUIM) is an extension of high-utility itemset mining (HUIM), which provides a reliable measure to reveal utility patterns by considering the length of the mined pattern. Some research has been conducted to improve the efficiency of mining by designing a variety of pruning strategies and effective frameworks, but few works have focused on the maintenance algorithms in the dynamic environment. Unfortunately, most existing works of HAUIM still have to rescan databases multiple times when it is necessary. In this paper, the pre-large concept is used to update the discovered HAUIs in the newly inserted transactions and reduce the time of the rescanning process. To further improve the performance of the developed algorithm, two new upper-bounds are also proposed to decrease the number of candidates for HAUIM. Experiments were performed to compare the previous Apriori-like method and the proposed APHAUP algorithm with the two new upper-bounds in terms of the number of maintenance patterns and runtime in several datasets. The experimental results show that the proposed APHAUP algorithm has excellent performance and good potential to be applied in real applications.

A 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.

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task. As a result, too many closed high-utility itemsets (CHUIs) will not only increase the time and memory consumption but also make it difficult for users to analyze the results. To address this problem, this paper proposes an efficient algorithm called FCHUIM, which is used to mine a concise representation called the frequent closed high-utility itemset (FCHUI). FCHUIM is based on a total summary list structure for storing and retrieving utility lists without repeatedly scanning the database. In addition, the proposed algorithm uses an effective upper bound on the utility of items to efficiently reduce the search space. Furthermore, a precheck method and a nested list structure are also introduced in this work to quickly discover FCHUIs. To test the performance of FCHUIM, we conduct extensive experiments on real-life and synthetic databases. We also select two state-of-the-art algorithms to compare with FCHUIM: CHUI-Miner and CLS-Miner. The results show that FCHUIM performs well on databases, whether the databases are dense or sparse.