Frequent subgraph mining is fundamental in graph mining, with wide-ranging applications in domains such as biology, chemistry, and social network analysis. Most existing algorithms are tailored for static graph databases. Real-world databases often exhibit dynamic attributes, such as data that may change over time. Existing methods for mining frequent subgraphs in databases with dynamic attributes primarily cater to dynamic graph databases, in which graphs evolve over time. However, in practice, a category of graph databases allows for adding or removing graphs. We refer to these databases as dynamic ones, which can be incrementally or decrementally updated while the remaining graphs do not change. This paper introduces frequent subgraph mining in this type of database and proposes the corresponding algorithm called DyFSM. We design a set called Fringe, which comprises DMFSand DMIS. DMFSis a novel concise representation based on the DFS code and can efficiently recover all frequent subgraphs. DMISis a set of subgraphs from which all infrequent subgraphs can be extended. Fringefacilitates updating frequent subgraphs in the renewed database. In our experiments, we collect four real-world graph datasets and conduct experiments using DyFSM. The results validate the accuracy and show good performance of our algorithm.

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.

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.