Minimum Utility Threshold Research Articles

Utility Mining across Multi-Sequences with Individualized Thresholds

Utility-oriented pattern mining is an emerging topic, since it can reveal high-utility patterns from different types of data, which provides more information than the traditional frequency/confidence-based pattern mining models. The utilities of various items/objects are not exactly equal in realistic situations; each item/object has its own utility or importance. In general, the user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while theminutilis set extremely high or low. We first design a new utility mining framework namely USPT for mining high-<u>U</u>tility <u>S</u>equential <u>P</u>atterns across multi-sequences with individualized <u>T</u>hresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results show that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.

Mining High-utility Temporal Patterns on Time Interval–based Data

In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining , to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.

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.

ProUM: Projection-based utility mining on sequence data

Utility is an important concept in Economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.

A BPSO-based method for high-utility itemset mining without minimum utility threshold

High-utility itemset mining is used to obtain high utility itemsets by taking into account both the quantity as well as the utility of each item, which have not been considered in frequent itemset mining. Many algorithms compute high utility itemsets by setting a minimum utility threshold in advance. However, determining the minimum utility threshold is not easy. Too high or too low a threshold may result in incorrect high utility itemsets. In this paper, we propose a method based on binary particle swarm optimization to optimize the search for high utility itemsets without setting the minimum utility threshold beforehand. Instead, the application of the minimum utility threshold is performed as a post-processing step. Experiments on five datasets indicate that the proposed method is better than existing methods in finding high utility itemsets, and the time to obtain those itemsets is faster than that with setting the minimum utility threshold first.

SSUP-Growth: A Novel Mining High Utility Algorithm Itemset with Single-Scan of Database

High Utility Itemset Mining (HUIM) alludes to the identification of itemsets of high utility in the value-based database UP-Growth algorithm is a standout amongst the best algorithms for overcome the challenge of candidate generation and scan database reputedly of previous algorithms. However, it needs scan database twice to actualize the UP tree. Regarding of the updating existing data with new information, UP-growth needs for twofold scanning of new information and existing information. The fundamental motivation behind this work is to build up another algorithm, Single-Scan Utility Pattern Tree (SSUP-tree), for mining high utility itemsets from transaction database through only single-scan of database. In our algorithm, the details of high-utility itemsets is preserved in a particular data structure of the SSUP-Tree after a single-scan of database. Consequently, it can retrieve the identical UP-tree with a fixed minimum utility threshold. The proposed algorithm required to scan the new data only to update SSUP-tree. In this regard, in order to estimate the execution of the proposed algorithm, the SSUP-tree algorithm has been implemented on synthetic and real datasets. The results of this study revealed that SSUP-tree shows a significant enhancement in the execution in terms of runtime since it keeps the huge databases details in a compact format and it avoids repetition of database scanning.

FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences

Mining all frequent high utility sequences (FHUS) in quantitative sequential databases (QSDBs) is a generalization of the problem of mining all frequent sequences in non-quantitative sequence databases. In the last decade, the former problem has attracted the attention of many researchers because utility-based sequences are more informative and actionable for decision-making than frequent sequences. Although utility-based sequences have many real-life applications, their number is often very large, especially for low minimum utility thresholds and long sequences. It can thus be difficult for users to analyze them and mining utility-based sequences often requires much time and memory. To solve this problem, this paper proposes two concise representations of FHUS, having a small cardinality that provides a concise summary of all FHUS. Those representations are defined as two sets, FCHUS and FMaxHUS, of all frequent maximal and closed high utility sequences. To efficiently mine these concise representations, two width and depth pruning strategies are proposed for eliminating low utility sequences early and a novel local pruning strategy is proposed named LPCHUS using a new extended measure on projected databases for eliminating non-closed and non-maximal high utility sequences early as well as their extensions. Based on these strategies and a novel data structure named SIDUL in vertical format, an algorithm named FMaxCloHUSM is designed for efficiently mining the sets of FCHUS and FMaxHUS, separately or simultaneously. To our best knowledge, this is the first algorithm for discovering these two concise representations. An experimental study conducted using both real-life and synthetic QSDBs shows that the proposed algorithm is efficient in terms of time and memory consumption, and that the developed strategies greatly reduce the search space.

AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY RARE ITEMSETS OVER UNCERTAIN DATABASES

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention. Association Rule Mining (ARM) is a well known and most popular technique of Data Mining. It identifies itemsets from the dataset which appears frequently and generates association rules. This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets. In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue. Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain. High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest. In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases. This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al. The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.

HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS

The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQS-DBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to early eliminate low utility or low probability sequences as well as their extensions, and to reduce sets of candidate items for extensions during the mining process. Based on these strategies, a novel ecient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted in both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption.

Stellar mass black hole optimisation for utility mining

Major challenges in mining high utility itemsets from the transaction databases requires exponential search space and database-dependent minimum utility threshold. The search space is very large because of the large number of distinct items and size of the database. Data analysts need to specifying appropriate minimum utility thresholds for their data mining tasks though they may have no knowledge pertaining to their databases. To get rid of these problems, Stellar mass black hole optimisation (SBO) method is proposed to mine Top-K HUIs from the transaction database without specifying minimum utility threshold. To know the performance of SBO, the experiment results are compared with GA.

Improved Genetic Algorithm for High-Utility Itemset Mining

High-utility itemset mining (HUIM) is an important research topic in the data mining field. Typically, traditional HUIM algorithms must handle the exponential problem of huge search space when the database size or number of distinct items is very large. As an alternative and effective approach, evolutionary computation (EC)-based algorithms have been proposed to solve HUIM problems because they can obtain a set of nearly optimal solutions in limited time. However, it is still time-consuming for EC-based algorithms to find complete high-utility itemsets (HUIs) in transactional databases. To address this problem, we propose an HUIM algorithm based on an improved genetic algorithm (HUIM-IGA). In addition, a neighborhood exploration strategy is proposed to improve search efficiency for HUIs. To reduce missing HUIs, a population diversity maintenance strategy is employed in the proposed HUIM-IGA. An individual repair method is also introduced to reduce invalid combinations for discovering HUIs. In addition, an elite strategy is employed to prevent the loss of HUIs. Experimental results obtained on a set of real-world datasets demonstrate that the proposed algorithm can find complete HUIs in terms of the given minimum utility threshold, and the time-consuming of HUIM-IGA is relatively lower when mining the same number of HUIs than state-of-the-art EC-based HUIM algorithms.

TKEH: an efficient algorithm for mining top-k high utility itemsets

High utility itemsets mining is a subfield of data mining with wide applications. Although the existing high utility itemsets mining algorithms can discover all the itemsets satisfying a given minimum utility threshold, it is often difficult for users to set a proper minimum utility threshold. A smaller minimum utility threshold value may produce a huge number of itemsets, whereas a higher one may produce a few itemsets. Specification of minimum utility threshold is difficult and time-consuming. To address these issues, top-k high utility itemsets mining has been defined where k is the number of high utility itemsets to be found. In this paper, we present an efficient algorithm (named TKEH) for finding top-k high utility itemsets. TKEH utilizes transaction merging and dataset projection techniques to reduce the dataset scanning cost. These techniques reduce the dataset when larger items are explored. TKEH employs three minimum utility threshold raising strategies. We utilize two strategies to prune search space efficiently. To calculate the utility of items and upper-bounds in linear time, TKEH utilizes array-based utility technique. We carried out some extensive experiments on real datasets. The results show that TKEH outperforms the state-of-the-art algorithms. Moreover, TKEH always performs better for dense datasets.

Mining top-k high utility itemsets with effective threshold raising strategies

Top-K High Utility Itemset (HUI) mining problem offers greater flexibility to a decision maker in specifying her/his notion of item utility and the desired number of patterns. It obviates the need for a decision maker to determine an appropriate minimum utility threshold value using a trial-and-error process. The top-k HUI mining problem, however, is more challenging and requires use of effective threshold raising strategies. Several threshold raising strategies have been proposed in the literature to improve the overall efficiency of mining top-k HUIs. This paper advances the state-of-the-art and presents a new Top-K HUI method (THUI). A novel Leaf Itemset Utility (LIU) structure and a threshold raising strategy is proposed to significantly improve the efficiency of mining top-k HUIs. A new utility lower bound estimation method is also introduced to quickly raise the minimum utility threshold value. The proposed THUI method is experimentally evaluated on several benchmark datasets and compared against two state-of-the-art methods. Our experimental results reveal that the proposed THUI method offers one to three orders of magnitude runtime performance improvement over other related methods in the literature, especially on large, dense and long average transaction length datasets. In addition, the memory requirements of the proposed method are found to be lower.

On Incremental High Utility Sequential Pattern Mining

High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner + algorithm to mine HUSPs incrementally. Specifically, to avoid redundant re-computations, we propose a tighter upper bound of the utility of a sequence, called Tight Sequence Utility (TSU), and then we design a novel data structure, called the candidate pattern tree, to buffer the sequences whose TSU values are greater than or equal to the minimum utility threshold in the original database. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of concise utility information is designed to be stored in each tree node. To improve the mining efficiency, several strategies are proposed to reduce the amount of computation for utility update and the scopes of database scans. Moreover, several strategies are also proposed to properly adjust the candidate pattern tree for the support of multiple database updates. Experimental results on some real and synthetic datasets show that IncUSP-Miner + is able to efficiently mine HUSPs incrementally.

A pure array structure and parallel strategy for high-utility sequential pattern mining

High-utility sequential pattern mining (HUSPM) is the task of discovering all sequential patterns in a sequence database whose utility values are equal to or greater than a given minimum utility threshold. HUSPM has become increasingly important in many real-world data mining applications, such as market basket data analysis, weblog mining, and bio-medical gene data analysis, which considers co-occurrence values and quantity, utility (e.g., profit or cost) and time. Current approaches in the literature for HUSPM use the utility matrix to store a sequence database in the memory. Unfortunately, the utility matrix consumes a large amount of main memory. To address this issue, we introduce a pure array structure that reduces the memory consumption when compared to the utility matrix. In addition, HUSPM is also challenged with the downward closure property (DCP) to prune the search space. Recently, HUSPM algorithms have used the upper bound of utility values as the DCP. However, it is usually higher than the actual utility of patterns. Thus, these algorithms may generate many candidate patterns. The large search space leads to poor performance due to excessive runtime and memory usage. One of the reasons is the number of candidate patterns is proportional to the number of requisite projected database scans for calculating their actual utilities. In this paper, we present a novel pruning strategy that efficiently prunes non-HUSPs and significantly reduces the search space compared to the state-of-the-art HUS-Span algorithm. Moreover, we propose a parallel strategy to speed up the mining process. Then, we propose two algorithms which are the pure Array structure for High-utility Sequential (AHUS) pattern mining and AHUS parallel mining (AHUS-P). The AHUS-P algorithm uses shared memory to parallelize the mining process. It concurrently identifies HUSPs based on the advantages of the multi-core processor architecture. The experimental results show that AHUS and AHUS-P can efficiently and effectively discover all HUSPs. Both the proposed algorithms outperform the state-of-the-art HUS-Span algorithm in terms of runtime, memory usage, and scalability for all experimental datasets.

An efficient algorithm for Hiding High Utility Sequential Patterns

High Utility Sequential Patterns (HUSP) are a type of patterns that can be found in data collected in many domains such as business, marketing and retail. Two critical topics related to HUSP are: HUSP mining (HUSPM) and HUSP Hiding (HUSPH). HUSPM algorithms are designed to discover all sequential patterns that have a utility greater than or equal to a minimum utility threshold in a sequence database. HUSPH algorithms, by contrast, conceal all HUSP so that competitors cannot find them in shared databases. This paper focuses on HUSPH. It proposes an algorithm named HUS-Hiding to efficiently hide all HUSP. An extensive experimental evaluation is conducted on six real-life datasets to evaluate the performance of the proposed algorithm. According to the experimental results, the designed algorithm is more effective than three state-of-the-art algorithms in terms of runtime, memory usage and hiding accuracy.

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.

Mining of high utility-probability sequential patterns from uncertain databases.

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds.

Mining High-Utility Itemsets Based on Multiple Minimum Support and Multiple Minimum Utility Thresholds

Mining High-Utility Itemsets Based on Multiple Minimum Support and Multiple Minimum Utility Thresholds

High Fuzzy Utility Based Frequent Patterns Mining Approach for Mobile Web Services Sequences

Nowadays high fuzzy utility based pattern mining is an emerging topic in data mining. It refers to discover all patterns having a high utility meeting a user-specified minimum high utility threshold. It comprises extracting patterns which are highly accessed in mobile web service sequences. Different from the traditional fuzzy approach, high fuzzy utility mining considers not only counts of mobile web service accessed in a sequence but additionally their preference value while mobile web services sequences are accessed. In this paper, I introduce a new approach, namely HFUBPM (High Fuzzy Utility Based Patterns Mining) for high fuzzy utility patterns extraction from mobile web services accessed sequences. The proposed approach uses a fuzzy minimum operator to extract highly interesting patterns from web service accessed sequences. In this proposed approach, downward closure property in fuzzy sets is handled by an efficient upper bound model. This model improves the efficiency of mining way. At last, the experiments have been performed on both synthetic and real datasets, which show that the proposed approach has good performances in terms of execution and search space.