Title: GPAR: Gaussian process based association rule mining
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: association rule, Gaussian process, data mining

Abstract: We introduce GPAR, a Gaussian process–based framework for association‐rule mining. Unlike traditional AR mining methods which treat items as atomic symbols, GPAR represents each item by a feature vector and fits a Gaussian process to its latent membership variable using transaction data. Uncertainty estimates are obtained by sampling from the GP posterior and marginalizing over irrelevant latent dimensions; unobserved itemsets can be inferred without re-processing. Through experiments with various kernel choices, GPAR outperforms classic AR‐mining algorithms in identifying small or rare itemsets, quantifying and propagating uncertainty, incorporating prior knowledge to capture complex patterns, and inferring new frequent itemsets without incurring additional computational cost.

TL;DR: We introduce a new association‐rule mining method using Gaussian process, improving complex pattern identification with prior knowledge, uncertainty quantification, custom kernel design, and reduced cost for inferring new frequent itemsets.