Association mining method based on neighborhood perspective

作者：Honghong Cheng, Jiye Liang, Yuhua Qian, Zhiguo Hu · 发表于：Scientia Sinica Informationis · 年份：2020 · DOI：10.1360/ssi-2020-0009 · 被引用次数：11 · 研究领域：Data Mining Algorithms and Applications、Rough Sets and Fuzzy Logic

Important tasks in big data association mining are identification of potentially complex associations among massive variables and determination of the strength of different forms of associations. However, uncertain data distributions and diverse associations make it difficult to ensure the applicability and accuracy of measures based on distribution assumptions and data-driven non-parametric measurement methods. Therefore, an effective association measure that is unbiased relative to relationship types is urgently needed. In this article, starting from the fair ordering requirement of potential relationships in big data, we review the current axiomatic conditions of association metrics, provide some possible properties that association measures in big data should satisfy, discuss some limitations of two types of association methods based on neighborhood perspective, and propose a new association measure based on $k$-NN granule, which we refer to as maximum neighborhood coefficient. Experiments using artificial and real datasets verify the effectiveness and superiority of the proposed method from different perspectives. Finally, we identify interesting phenomena in the experiment and theoretical issues to be solved that we hope will motivate deeper thinking and research in this field.