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High Utility Pattern Mining is a popular task for analyzing data. It consists of discovering patterns having a high importance in databases. A popular application of high utility pattern mining is to identify high utility (profitable) patterns in customer transaction data. Though such analysis can be useful to understand data, it does not consider the cost (e.g. effort, resources, money or time) required for obtaining the utility (benefits). In this paper, we argue that to discover interesting patterns in event sequences, it is useful to consider both a utility model and a cost model. For example, to identify cost-effective ways of treating patients from medical pathways data, it is desirable to consider not only the ability of treatments to inhibit symptoms or cure a disease (utility) but also the resources consumed and the time spent (cost) to provide these treatments. Based on this perspective, this paper defines a novel task of discovering Cost-Effective Event Sequences in event logs. In this task, cost is modeled as numeric values, while utility is represented either as binary or numeric values. Measures are proposed to evaluate the trade-off and correlation between cost and utility of patterns to identify cost-effective patterns (patterns having a low cost but providing a high utility). Three efficient algorithms called CEPB, corCEPB and CEPN are designed to extract these patterns. They rely on a tight lower-bound on the cost and a memory buffering technique to find patterns efficiently. Experiments show that the proposed algorithms achieve high efficiency, that proposed optimizations improve efficiency, and that insightful cost-effective patterns are found in real-life e-learning data. [ABSTRACT FROM AUTHOR]

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment. [ABSTRACT FROM AUTHOR]