Neighborhood Information-Based Method for Multivariate Association Mining

作者：Cheng, Honghong; Qian, Yuhua^*; Guo, Yingjie; Zheng, Keyin; Zhang, Qingfu

来源：IEEE Transactions on Knowledge and Data Engineering, 2023, 35(6): 6126-6135.

DOI：10.1109/TKDE.2022.3178090

• Entropy
• Spirals
• Noise measurement
• Mutual information
• Knowledge engineering
• Data mining
• Data engineering
• Association mining
• multivariate association measure
• distribution-free
• nonparametric
• neighborhood information

Most current data is multivariable, exploring and identifying valuable information in these datasets has far-reaching impacts. In particular, discovering meaningful hidden association patterns in multivariate plays an important role. Plenty of measures for multivariate association have been proposed, yet it is still an open research challenge for effectively capturing association patterns among three or more variables, especially the scenario without any prior knowledge about those relationships. To do so, we desire a distribution-free, association type-independent and non-parametrical measure. For practical applications, such a measure should comparable, interpretable, scalable, intuitive, reliability, and robust. However, no exiting measures fulfill all of these desiderata. In this paper, taking advantage of the neighborhood information of a sample, we propose MNA, a maximal neighborhood multivariate association measure that satisfies all the above criteria. Extensive experiments on synthetic and real data show it outperforms state-of-the-art multivariate association measures.