Abstract
In data mining, the U-Apriori algorithm is typically used for Association Rule Mining (ARM) from uncertain data. However, it takes too much time in finding frequent itemsets from large datasets. This paper proposes a novel algorithm based on Self-Organizing Map (SOM) clustering for ARM from uncertain data. It supports the feasibility of neural network for generating frequent itemsets and association rules effectively. We take transactions in which itemsets are associated with probabilities of occurrence. Each transaction is converted to an input vector under a probabilistic framework. SOM is employed to train these input vectors and visualize the relationship between the items in a database. Distance map based on the weights of winning neurons and support count of items is used as a criteria to prune data space. As shown in our experiments, the proposed SOM is a promising alternative to typical mining algorithms for ARM from uncertain data.
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Mansha, S., Babar, Z., Kamiran, F., Karim, A. (2016). Neural Network Based Association Rule Mining from Uncertain Data. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_16
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DOI: https://doi.org/10.1007/978-3-319-46681-1_16
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