High Utility Sequence Research Articles

Mining high-utility sequences with positive and negative values

Sequence pattern discovery is a fundamental topic in the domain of data mining. It has been widely used to solve various problems (e.g., behavior pattern discovery, gene pattern discovery in bioinformatics, user click pattern mining, etc.). High-utility sequence mining as a novel hot issue is more challenging and has generally attracted plenty of attention. Our paper focuses on mining high-utility sequences in a more complicated environment with high efficiency. Most of the previous methods for utility mining aim to find high-utility sequences suitable for items with positive values, but most real-world situations contain items with both positive and negative values. Several algorithms have been applied to the above sophisticated situation and can be used as our comparing algorithms. In this paper, we introduce the FHUSN (Fast mining High Utility Sequences with Negative item) algorithm to mine high-utility sequences in situations with or without negative utility values. FHUSN utilizes the new utility array to store data. Several new pruning strategies that apply to situations with or without negative values have been used to reduce search space. Experiments are carried out on several benchmark datasets, and experimental results illustrate that our method has better performance.

TUSQ: Targeted High-Utility Sequence Querying

Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds (suffix remain utility and terminated descendants utility) as well as a vertical last instance table. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.

A Survey on Analysis of Data Mining Algorithms for High Utility Itemsets

High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most of the measurements are expected. In real time applications, such as the market basket review, company decision making and web administration organization results, large quantities of datasets are slowly evolving with new knowledge incorporated. The usual mining calculations cannot handle such complex databases and retrieve useful data. The essential task of data collection in a quantifiable sequence dataset is to determine entirely high utility sequences. The number of sequences found is always extremely high, though useful. This article studies the issue of the mining of repeated high utility sequence that meet item restrictions in order to identify patents that are more suited to the needs of a customer. Also, this article introduces high-value element set mining, examines modern algorithms, their extensions, implementations, and explores research opportunities.

Explainable Fuzzy Utility Mining on Sequences

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability and interpretability. To obtain a human-explainable data intelligence model for decision making, in this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and, thus, expedite the mining process. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.

Efficient algorithms for mining frequent high utility sequences with constraints

An important data mining task is to discover all high utility sequences in a quantitative sequence database. Although useful, the number of discovered sequences is often very large. To find patterns that are more tailored to a user’s needs, this paper studies the problem of mining frequent high utility sequences satisfying item constraints. This article proposes a novel algorithm named C-FHUSM to quickly obtain these sequences from two concise representations discovered from a quantitative sequence database, namely frequent generator high utility sequences and frequent closed high utility sequences. The first set is extracted using a novel algorithm named FGenHUSM, while an existing algorithm is applied to extract the second set. C-FHUSM integrates novel pruning techniques to ignore sequences that do not satisfy item constraints early by checking only a small number of representative sequences at the beginning of the mining process. Experimental results show that C-FHUSM can be more than ten times faster and has better scalability than a modified version of the state-of-the-art EHUSM algorithm for mining sequences with item constraints. Moreover, it is found that using C-FHUSM is beneficial when a user frequently changes constraints as results can be updated without rescanning the database.

Mining top-N high-utility operation patterns for taxi drivers

In recent years, the rapid development of mobile network and wireless sensor technology has brought opportunities to change the way of the existing taxi business operation. How to improve the operation revenues of taxi drivers has become a topic worthy of research. This paper analyzes and mines taxi operation data to provide taxi drivers with personalized sequence recommendation services, thereby increasing their expected revenues. Different from previous works, the proposed method in this paper recommends a series of future operation orders for taxi drivers, instead of recommending several discrete locations for the current order. In this paper, firstly, by performing spatial-temporal clustering on the origins and destinations of passengers, the spatial and temporal distribution characteristics of passengers in the city are identified. Secondly, the origin of the current passenger is used as the root node to construct a top-N high-utility sequence tree, and this process can be divided into two processes: top-down building tree and bottom-up sorting path utility. The two pruning strategies of node utility and path utility are used to reduce the generation of candidate sets. Finally, a series of potential orders based on dynamic context are recommended to taxi drivers, so as to maximize the expected revenues of taxi drivers. The experimental results demonstrate that there is a close relationship between taxi drivers’ operation behavior patterns and their revenues. The proposed system framework and algorithm in this paper can effectively mine global and long-term top-N high-utility operation patterns.

An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (s-sequences) as high-utility hierarchical sequential patterns from g-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.

EHAUSM: An efficient algorithm for high average utility sequence mining

Identifying high utility sequences in a quantitative sequence database is an important data mining task. However, a key problem of current approaches is that extensions of a high utility sequence often have a high utility. Hence, traditional techniques are often biased toward finding long patterns. To circumvent this problem, this paper proposes techniques for the problem of high average-utility sequence (HAUS) mining (HAUSM). HAUSs are more meaningful than high utility sequences because the former are found using the average-utility measure, which consider the length of patterns in utility calculations. HAUSM is more general than high average-utility itemset mining but it is also a more difficult problem because the downward-closure property used for search space reduction does not hold for the average-utility. To overcome that challenge, this paper introduces two upper bounds and a weak upper bound on the average-utility measure, and four width pruning, depth pruning, reducing, and tightening strategies. These strategies are designed to eliminate candidate HAUSs to speed up HAUS mining. Based on these theoretical results, a novel algorithm named EHAUSM is proposed for HAUSM. Experiments on both real-life and synthetic quantitative sequence databases have confirmed its efficiency in terms of memory consumption and runtime.

Distributed mining of high utility time interval sequential patterns using mapreduce approach

High Utility Sequential Pattern mining (HUSP) algorithms aim to find all the high utility sequences from a sequence database. Due to the large explosion of data, recently few distributed algorithms have been designed for mining HUSPs based on the MapReduce framework. However, the existing HUSP algorithms such as USpan, HUS-Span and BigHUSP are able to predict only the order of items, they do not predict the time between the items, that is, they do not include the time intervals between the successive items. But in a real-world scenario, time interval patterns provide more valuable information than conventional high utility sequential patterns. Therefore, we propose a distributed high utility time interval sequential pattern mining (DHUTISP) algorithm using the MapReduce approach that is suitable for big data. DHUTISP creates a novel time interval utility linked list data structure (TIUL) to efficiently calculate the utility of the resulting patterns. Moreover, two utility upper bounds, namely, remaining utility upper bound (RUUB) and co-occurrence utility upper bound (CUUB) are proposed to prune the unpromising candidates. We conducted various experiments to prove the efficiency of the proposed algorithm over both the distributed and non-distributed approaches. The experimental results show the efficiency of DHUTISP over state-of-the-art algorithms, namely, BigHUSP, AHUS-P, PUSOM and UTMining_A.

High average-utility sequential pattern mining based on uncertain databases

The emergence and proliferation of the internet of things (IoT) devices have resulted in the generation of big and uncertain data due to the varied accuracy and decay of sensors and their different sensitivity ranges. Since data uncertainty plays an important role in IoT data, mining the useful information from uncertain dataset has become an important issue in recent decades. Past works focus on mining the high sequential patterns from the uncertain database. However, the utility of a derived sequence increases along with the size of the sequence, which is an unfair measure to evaluate the utility of a sequence since any combination of a high-utility sequence will also be the high-utility sequence, even though the utility of a sequence is merely low. In this paper, we address the limitation of the previous potential high-utility sequential pattern mining and present a potentially high average-utility sequential pattern mining framework for discovering the set of potentially high average-utility sequential patterns (PHAUSPs) from the uncertain dataset by considering the size of a sequence, which can provide a fair measure of the patterns than the previous works. First, a baseline potentially high average-utility sequential pattern algorithm and three pruning strategies are introduced to completely mine the set of the desired PHAUSPs. To reduce the computational cost and accelerate the mining process, a projection algorithm called PHAUP is then designed, which leads to a reduction in the size of candidates of the desired patterns. Several experiments in terms of runtime, number of candidates, memory overhead, number of discovered pattern, and scalability are then evaluated on both real-life and artificial datasets, and the results showed that the proposed algorithm achieves promising performance, especially the PHAUP approach.

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.

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.

Mining High Utility Sequential Patterns with Negative Item Values

High utility sequential patterns (HUSP) refer to those sequential patterns with high utility (such as profit), which play a crucial role in many real-life applications. Relevant studies of HUSP only consider positive values of sequence utility. In some applications, however, a sequence consists of items with negative values (NIV). For example, a supermarket sells a cartridge with negative profit in a package with a printer at higher positive return. Although a few methods have been proposed to mine high utility itemsets (HUI) with NIV, they are not suitable for mining HUSP with NIV because an item may occur more than once in a sequence and its utility may have multiple values. In this paper, we propose a novel method High Utility Sequential Patterns with Negative Item Values (HUSP-NIV) to efficiently mine HUSP with NIV from sequential utility-based databases. HUSP-NIV works as follows: (1) using the lexicographic quantitative sequence tree (LQS-tree) to extract the complete set of high utility sequences and using I-Concatenation and S-Concatenation mechanisms to generate newly concatenated sequences; (2) using three pruning methods to reduce the search space in the LQS-tree; (3) traversing LQS-tree and outputting all the high utility sequential patterns. To the best of our knowledge, HUSP-NIV is the first method to mine HUSP with NIV, which is shown efficient on both synthetic and real datasets.