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It is uncertain whether products will be manufactured as defective products. Producing the defective products causes the waste of time and finance in the industrial fields. When the financial crisis occurs in the manufacturing plants, production lines of the less profitable products should be stopped. To overcome the financial crisis, it is required to find the production lines that manufacture the less profitable commodities and are likely to produce the faulty merchandises and remove the corresponding production lines in order to increase the profit. Erasable pattern mining has been suggested to analyze and solve the financial problems by discovering patterns with low profit. As databases become more diverse due to the environment and characteristics of data, demands for erasable pattern mining that processes the various databases increase. An uncertain database, which is one of diverse types of databases, includes the probability related to the item, such as the probability of existence and the probability of defects. Erasable pattern mining, which considers the probability of each item, can discover pattern results which are more suitable for purpose of users. Motivated by this, we propose an efficient tree-based erasable pattern mining algorithm from the uncertain database. Considering the probability of the item, the suggested algorithm extracts erasable patterns with low profits from the uncertain database. Since both profit of the product and the probability of the item are considered, the discovered patterns are more meaningful than the patterns found by the existing erasable pattern mining. Moreover, the proposed method improves the mining operations by utilizing a list structure as well as a tree structure. We performed a performance evaluation on diverse real and synthetic datasets in order to prove the proposed algorithm outperforms the existing erasable pattern mining algorithms. The differences in runtime, memory usage, and scalability are shown through the comparison of performance with other state-of-the-art algorithms and the suggested algorithm. [ABSTRACT FROM AUTHOR]

High utility pattern mining (HUPM) discovers meaningful patterns by considering features of items and utility from non-binary data. Data called stream data is continually generated over time. Various techniques based on high utility pattern mining have been suggested for processing stream data. High utility pattern mining based on a sliding window performs pattern mining using a window. Since it uses only the data stored in a window, only the latest data can be managed. However, Stream data has the property that newly created data has a higher influence than relatively old data. It is necessary to consider the importance of the data stored in a window differently. In this paper, we propose an efficient algorithm based on a sliding window approach that mines high utility patterns considering the latest data more significantly from damped stream data where new data is constantly being inserted. In other words, our technique divides the stream data into fixed-sized multiple batch data and processes differently the importance of each batch data in a window according to the added time using the decaying factor. Moreover, we conduct experiments to compare and analyze our approach with the state-of-the-art algorithms using real and synthetic datasets. The experimental results show that our proposed method outperforms the competitors in terms of run time, memory usage, and scalability test. [ABSTRACT FROM AUTHOR]