Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization

This paper proposes a framework for fuzzy temporal association rule mining based on density clustering optimization, aimed at improving the fuzzification of continuous attributes and enhancing the efficiency of mining temporal association rules. The first part of the framework effectively handles continuous attribute data and reduces data redundancy by adopting a density peak clustering algorithm and designing an optimized membership function. The second part introduces a fuzzy list ranking structure combined with item lifespan to mine fuzzy temporal association rules. Experimental results demonstrate that the framework exhibits superior performance in mining fuzzy temporal association rules and outperforms advanced algorithms in handling continuous attributes.