Abstract
We present an approximate algorithm for finding frequent itemsets. The main idea can be described as turning the problem of mining frequent itemsets into a clustering problem. More precisely, we first represent each transaction by a vector using one-hot encoding scheme. Then, by means of mini batch k-means, we group all transactions into a number of clusters. The center of each cluster can be assumed as a potential candidate for a frequent itemset. To test the validity of this assumption, we compute the support of itemsets represented by cluster centers. All clusters that do not meet the minimum support condition will be removed from the set of clusters. As our experiments show, this approximate algorithm can capture more than 90% of all frequent itemsets at a much faster rate than the competing algorithms. Moreover, we show that the execution time of our algorithm is linear.
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Fatemi, S.M., Hosseini, S.M., Kamandi, A., Shabankhah, M. (2020). A Clustering Based Approximate Algorithm for Mining Frequent Itemsets. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_18
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