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Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities. This article is categorized under:Algorithmic Development > Spatial and Temporal Data MiningAlgorithmic Development > Association Rules [ABSTRACT FROM AUTHOR]

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242 This article is categorized under: Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation [ABSTRACT FROM AUTHOR]

Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining ( DM) provides powerful techniques for finding meaningful and useful information from a very large amount of data, and has a wide range of real-world applications. However, traditional DM algorithms assume that the data is centrally collected, memory-resident, and static. It is challenging to manage the large-scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment [also called distributed data mining (DDM)] has been emerging as a valuable alternative in many applications. In this study, a survey of state-of-the-art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment. WIREs Data Mining Knowl Discov 2017, 7:e1216. doi: 10.1002/widm.1216 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open-source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The cover image, by Wensheng Gan et al., is based on the Overview Data mining in distributed environment: a survey, DOI: . [ABSTRACT FROM AUTHOR]