Abstract
Learning meaningful and effective representations for transaction data is a crucial prerequisite for transaction classification and clustering tasks. Traditional methods which use frequent itemsets (FIs) as features often suffer from the data sparsity and high-dimensionality problems. Several supervised methods based on discriminative FIs have been proposed to address these disadvantages, but they require transaction labels, thus rendering them inapplicable to real-world applications where labels are not given. In this paper, we propose an unsupervised method which learns low-dimensional continuous vectors for transactions based on information of both singleton items and FIs. We demonstrate the superior performance of our proposed method in classifying transactions on four datasets compared with several state-of-the-art baselines.
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- 1.
Available at https://github.com/neo4j-examples/neo4j-foodmart-dataset.
- 2.
Since our method is unsupervised, we only compare it with unsupervised baselines.
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Acknowledgment
This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning. Tu Dinh Nguyen gratefully acknowledges the partial support from the Australian Research Council (ARC).
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Nguyen, D., Nguyen, T.D., Luo, W., Venkatesh, S. (2018). Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_29
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DOI: https://doi.org/10.1007/978-3-319-93040-4_29
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