Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytics, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.

A recently emerged branch of utility-based research, called high-utility quantitative itemset mining (HUQIM), has been widely applied in real-life, and it considers not only the utility factor but also the quantity with ranges of itemsets. However, most existing utility-mining algorithms assume that pat-terns always appear regardless of the period. For instance, some products may sell well at certain times of the year. Considering the rich information in the database, such as quantity and time, we propose an effective and efficient approach for discovering on-shelf high-utility quantitative itemsets (OHUQIs). To avoid scanning the database multiple times, we adopt a data structure to maintain some necessary information, and thus, OHUQI only accesses the database twice. Several pruning strategies are also designed to prune a large number of unpromising itemsets in advance to shrink the search space. Finally, the subsequent experimental results show that OHUQI performs well on several real-world datasets.

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.

High-frequency itemset mining (HFIM) and high-utility itemset mining (HUIM) aim to discover itemsets with high occurrence and high utility, respectively, in a transaction database. A number of efficient algorithms have been developed to identify these high-utility itemsets (HUIs) or high-frequency itemsets (HFIs). Such algorithms play an increasingly important role in many occasions especially for analysis in commercial enterprises. In this paper, we propose a new model called joint utility and frequency for pattern classification, and two new algorithms, namely UFCgenand UFCfast. Both algorithms are designed to categorize each itemset into different type of patterns by setting the minimum thresholds of utility and frequency. We compare these algorithms on two datasets. The experimental results show that both algorithms can successfully collect three different types of itemsets from all candidate itemsets based on frequency and utility, and the list-based UFCfastalgorithm outperforms the level-wise-based UFCgenalgorithm in terms of execution time.

As an important technique for dealing with transactional database in the field of data mining, high-utility itemset mining (HUIM) can be used to discover itemsets which have a high utility. However, it has a bias when towarding the item combinations which have more exhibition period since they have more opportunity to generate a high utility. To address this, the on-shelf time period of items need to be considered, thus on-shelf utility mining (OSUM) can be applied in the application which is more closer to the actual situation. Currently several models have been proposed to deal with the OSUM problem, but they still suffer from the requirement that it needs to maintain a massive candidates in memory and to scan database many times. In this paper, we propose an effective algorithm named OSUMI (On-Shelf Utility Mining from Itemset-based data) which can discover the on-shelf itemsets with high utility in a more practical way. More precisely, in order to avoid the problems of high memory consumption, OSUMI applies some properties of on-shelf utility. Besides, two upper-bounds named subtree utility and local utility are applied to prune the search space. Finally, an extensive experimental study on two real on-shelf datasets shows that our proposed algorithm can be significantly faster than the state-of-the-art algorithm for this mining task.

In the field of data science, utility-driven data mining has become an emergent intelligent technique with wide applications. The existing utility mining algorithms usually discover all the patterns satisfying a given minimum utility threshold. However, a huge number of return results is not intuitive, not interpretable, and not easy for users to understand. Besides, it is often difficult and time-consuming for users to set a proper minimum utility threshold that is quite sensitive to the mining results. To address these issues, the problem of top-k high-utility itemset mining has been studied. In this paper, we present an efficient algorithm (named TopHUI) for finding top-k high-utility itemsets from transactional database that contains both positive and negative utility. This algorithm utilizes the positive-and-negative utility-list (PNU-list) to store the compress information, including positive, negative, and remaining utility. Besides, several threshold raising strategies and pruning strategies are proposed to prune the search space. Finally, some extensive experiments were conducted to evaluate the performance of the proposed TopHUI algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency.