Data squashing as preprocessing in association rule mining - Fister, Iztok; Novak, Damijan; Verber, Domen (2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022)
Data squashing is a well-known preprocessing method in Machine Learning that enables construction of smaller datasets from the original ones and provides approximately the same results of data analysis as the original. The paper proposes a new data squashing method for Association Rule Mining based on the Cosine similarity and Euclidean distance similarity. The method was applied to three datasets from the UCI Machine Learning repository. The results showed that the proposed data squashing method is effective, scalable, and easy to use, and therefore represents a huge potential for use in practice.