Abstract
Mining frequent patterns focus on discover the set of items which were frequently purchased together, which is an important data mining task and has broad applications. However, traditional frequent pattern mining does not consider the characteristics of the customers, such that the frequent patterns for some specific customer groups cannot be found. Multidimensional frequent pattern mining can find the frequent patterns according to the characteristics of the customer. Therefore, we can promote or recommend the products to a customer according to the characteristics of the customer. However, the characteristics of the customers may be the continuous data, but frequent pattern mining only can process categorical data. This paper proposes an efficient approach for mining multidimensional frequent pattern, which combines the clustering algorithm to automatically discretize numerical-type attributes without experts.
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Lee, YS., Yen, SJ. (2013). Mining Multidimensional Frequent Patterns from Relational Database. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_6
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DOI: https://doi.org/10.1007/978-3-642-36546-1_6
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