Abstract
Data mining, the extraction of hidden predictive large amounts of data and picking out the relevant information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Association Rule Mining has become one of the core data mining tasks that used to show the relationship between data items. These relationships are not based on inherent properties of the data themselves like functional dependencies, but based on co-occurrence of the data items. Association rules are frequently used in telecommunication network, market and risk management, advertising and inventory control. Recently many advance techniques are researched for making association rule mining more efficient to proposing a new perspective development in the field of data mining. One of the latest topics in this area is mining the hidden pattern from existing collection of databases by implementing particle swarm optimization (PSO) approach for increasing mining efficiency, extending the notion of association rules, enhancing the parameter such as support and confidence. In this article, the various advancements in association rule mining using particle swarm optimization is discussed.
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Ankita, S., Shikha, A., Jitendra, A., Sanjeev, S. (2013). A Review on Application of Particle Swarm Optimization in Association Rule Mining. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_46
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DOI: https://doi.org/10.1007/978-3-642-35314-7_46
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