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Incremental high utility pattern mining with static and dynamic databases

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Abstract

Pattern mining is a data mining technique used for discovering significant patterns and has been applied to various applications such as disease analysis in medical databases and decision making in business. Frequent pattern mining based on item frequencies is the most fundamental topic in the pattern mining field. However, it is difficult to discover the important patterns on the basis of only frequencies since characteristics of real-world databases such as relative importance of items and non-binary transactions are not reflected. In this regard, utility pattern mining has been considered as an emergent research topic that deals with the characteristics. In real-world applications, meanwhile newly generated data by continuous operation or data in other databases for integration analysis can be gradually added to the current database. To efficiently deal with both existing and new data as a database, it is necessary to reflect increased data to previous analysis results without analyzing the whole database again. In this paper, we propose an algorithm called HUPID-Growth (High Utility Patterns in Incremental Databases Growth) for mining high utility patterns in incremental databases. Moreover, we suggest a tree structure constructed with a single database scan named HUPID-Tree (High Utility Patterns in Incremental Databases Tree), and a restructuring method with a novel data structure called TIList (Tail-node Information List) in order to process incremental databases more efficiently. We conduct various experiments for performance evaluation with state-of-the-art algorithms. The experimental results show that the proposed algorithm more efficiently processes real datasets compared to previous ones.

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Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under ICT/SW Creative research program supervised by the NIPA (National ICT Industry Promotion Agency) (NIPA-2014-H0502-14-3008) and the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2013-005682).

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  1. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun & Heungmo Ryang

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  1. Unil Yun
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Correspondence to Unil Yun.

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Yun, U., Ryang, H. Incremental high utility pattern mining with static and dynamic databases. Appl Intell 42, 323–352 (2015). https://doi.org/10.1007/s10489-014-0601-6

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