The discovery of utility-driven patterns is a valuable and difficult research topic. It can extract significant and interesting information from specific and varied databases, increasing the value of the services provided. In practice, the utility measure is often used to reflect the importance, profit, or risk of an object or pattern. In the database, while utility is a flexible criterion for patterns, it is also a somewhat limited criterion due to the overlook of utility sharing. This leads to the derived patterns only exploring partial and local knowledge in the database. Utility occupancy considers the problem of mining with high utility but low occupancy. However, existing studies are focused on itemsets that cannot reveal the temporal relationship of object occurrences. Therefore, this article first defines the concept of utility occupancy of sequence data and raises the problem of High-Utility Occupancy Sequential Pattern Mining (HUOSPM). Three dimensions, including frequency, utility, and occupancy, are comprehensively evaluated in HUOSPM. An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed. Furthermore, two data structures for storing pattern-related information, including Utility-Occupancy-List-Chain (UOL-Chain) and Utility-Occupancy-Table (UO-Table), are designed, and six upper bounds are proposed to improve efficiency. Extensive experiments are conducted to evaluate the efficiency and effectiveness of the novel algorithm. A specific case study is provided, and the effects of different upper bounds and pruning strategies are analyzed. The comprehensive results suggest that the HUOSPM task is useful and efficient.

In many real-world applications, sequential rule mining (SRM) can offer prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that can reveal the temporal relationship between objects. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on the problem of targeted mining. Targeted sequential rule mining aims to obtain those interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. It can further improve the efficiency of users in analyzing rules and reduce the consumption of computing resources. In this paper, we first present the relevant definitions of target sequential rules and formulate the problem of targeted sequential rule mining. Then, we propose an efficient algorithm called TaSRM. Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the baseline algorithm.

Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.

Discovering interesting sequential rules in the sequence database is quite important for a variety of fields, ranging from customer behavior analysis to intrusion detection. High utility sequential rule mining (HUSRM) was proposed to obtain more informative rules. Its goal is to find those sequential rules with high utility values and high confidence, i.e., HUSRs. As far as we know, a few algorithms are proposed to discover HUSRs. However, these algorithms do not fully consider the existence of repetitive items in the sequences of the database. In this paper, we propose an algorithm named USER to discover HUSRs in multi-sequences with the existence of repetitive items. A data structure called an occurrence information (OI)-list is designed to distinguish the different occurrences of items in a sequence. Moreover, the change in the upper bound value after the rule expansion is discussed in detail, which is complicated by the repetitive items. We also introduce two pruning strategies (ROOR and REIO-I) to optimize mining efficiency when there are too many repetitive items in the sequence. Finally, we conduct experiments on several datasets, and the results show that USER is able to discover HUSRs with more accurate utility values in an acceptable amount of time and memory consumption.

In data mining, high-utility sequential pattern mining (HUSPM) focuses more on the specific values of items than on their frequency, making it more practical in real-life scenarios. HUSPM with the contiguous constraint can be used to solve some applications requiring the sequence elements to occur consecutively. Due to device, environment, privacy issues, and other factors, the data is often not accurate, and traditional algorithms for mining high utility continuous sequence patterns (HUCSPs) do not perform well in handling uncertain data. To address this challenge, this paper presents a new algorithm named uncertain utility-driven contiguous pattern mining (UUCPM), which can discover HUCSPs efficiently and correctly. The algorithm is designed to obtain results from sequence data with uncertain probabilities set on the item level. Two tighter upper bounds on utility and corresponding pruning strategies are also proposed, which can effectively process and reduce the number of candidate patterns generated during pattern mining, thereby improving the performance of the mining process. Through extensive experiments, the proposed UUCPM algorithm has been verified for accuracy and performance, demonstrating its advanced properties.

High utility sequential pattern mining (HUSPM) is a critical research topic in data analytics (e.g., smart-city technologies), which takes into consideration three pivotal factors of data: timestamp, internal quantization, and external utility. Recently, a query-enabled HUSPM approach has been proposed, which aims to discover patterns based on a query sequence. However, this approach only works on static data and does not solve the tasks well under dynamic data. When the data is updated, it needs to restart the mining process, which leads to a lot of duplicate calculations and resource consumption. In the paper, to address the mining task of increasing sequence data over time, we develop an Incremental Targeted HUSPM algorithm called ITUS. A tighter upper bound called tight extension sequence utility (TESU) is proposed to determine key candidates, which can avoid the generation of unpromising patterns. By using TESU, a target candidate pattern tree (TCP-tree) is utilized to record the sequence information, and several efficient strategies are implemented to incrementally update the tree. Finally, we extensively evaluate our proposed algorithm on both real-world and synthetic datasets. The experimental results clearly demonstrate that not only does the novel algorithm guarantee the accuracy of the results after multiple database updates, but it also achieves higher efficiency than the baseline approach.

Utility-driven mining is an important task in data science and has many applications in real life. High-utility sequential pattern mining (HUSPM) is one kind of utility-driven mining. It aims at discovering all sequential patterns with high utility. However, the existing algorithms of HUSPM can not provide a relatively accurate probability to deal with some scenarios for prediction or recommendation. High-utility sequential rule mining (HUSRM) is proposed to discover all sequential rules with high utility and high confidence. There is only one algorithm proposed for HUSRM, which is not efficient enough. In this article, we propose a faster algorithm called US-Rule, to efficiently mine high-utility sequential rules. It utilizes the rule estimated utility co-occurrence pruning strategy (REUCP) to avoid meaningless computations. Moreover, to improve its efficiency on dense and long sequence datasets, four tighter upper bounds (LEEU, REEU, LERSU, and RERSU) and corresponding pruning strategies (LEEUP, REEUP, LERSUP, and RERSUP) are designed. US-Rule also proposes the rule estimated utility recomputing pruning strategy (REURP) to deal with sparse datasets. Finally, a large number of experiments on different datasets compared to the state-of-the-art algorithm demonstrate that US-Rule can achieve better performance in terms of execution time, memory consumption, and scalability.

Correlated high-utility pattern mining (CoUPM) considers the correlation between items in a pattern and offers a more reliable analysis for users. In real-world applications, the discovered patterns from CoUPM can present more interpretable information, but not all of them are useful. Generally, users pay attention to the number of items a pattern contains, which allows them to make reasonable decisions. In this paper, we solve the problem of mining those correlated high-utility patterns whose length is specified. A utility-list-based algorithm called Flexible Correlated Utility-based Pattern (FCoUP) is proposed. Furthermore, we propose some pruning strategies with the designed upper bounds for the two evaluation metrics: correlation and utility, reducing unwanted patterns generated and nodes visited during the mining process. Experiments show that FCoUP variants can produce more intelligent and flexible correlated high-utility patterns on a variety of datasets.

Sequential rule mining (SRM) is an alternative to sequential pattern mining (SPM) when dealing with sequence data. SRM has a wide range of applications in numerous data analysis scenarios. Existing SRM algorithms usually discover the entire set of rules in the databases, which makes it not only difficult to analyze results because the discovered set is too large, but also does not consider the user’s expectations and background knowledge. To tackle this problem, researchers have explored related algorithms with different constraints according to their requirements. In this paper, we propose a flexible constraint-based SRM algorithm called ConSRM for discovering only the sequential rules within user-specified time bounds in a sequence database. This algorithm uses an efficient rule-growth method and develops corresponding constraints and pruning strategies to reduce the search space and speed up calculation. Comprehensive experiments were carried out on four real datasets to evaluate the performance (both effectiveness and efficiency) of ConSRM.

Negative sequential pattern (NSP) mining is crucial and sometimes carries more enlightening information than positive sequential pattern (PSP) mining in data mining. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often much more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. However, most algorithms only consider a single support, thus can not present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance and it uses less memory on large datasets compared to the state-of-the-art algorithm.