Abstract
Pattern mining is a key subfield of data mining that aims at developing algorithms to discover interesting patterns in databases. The discovered patterns can be used to help understanding the data and also to perform other tasks such as classification and prediction. After more than two decades of research in this field, great advances have been achieved in terms of theory, algorithms, and applications. However, there still remains many important challenges to be solved and also many unexplored areas. Based on this observations, this paper provides an overview of six key challenges that are promising topics for research and describe some interesting opportunities. Those challenges were identified by researchers from the field, and are: (1) mining patterns in complex graph data, (2) targeted pattern mining, (3) repetitive sequential pattern mining, (4) incremental, stream, and interactive pattern mining, (5) heuristic pattern mining, and (6) mining interesting patterns.
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Fournier-Viger, P. et al. (2022). Pattern Mining: Current Challenges and Opportunities. In: Rage, U.K., Goyal, V., Reddy, P.K. (eds) Database Systems for Advanced Applications. DASFAA 2022 International Workshops. DASFAA 2022. Lecture Notes in Computer Science, vol 13248. Springer, Cham. https://doi.org/10.1007/978-3-031-11217-1_3
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