Accelerating pattern mining on fuzzy data by packing truth values into blocks of bits

In pattern mining from tabular data using fuzzy logic, a common task involves computing triangular norms (t-norms) to represent conjunctions of fuzzy predicates and summing the resulting truth values to evaluate rule support or other pattern quality measures. Building on previous work, this paper presents an approach that packs multiple truth values into a single integer and performs t-norm computations directly on this compact representation. By using 4-, 8-, or 16-bit precision, the method substantially reduces memory consumption and improves computational efficiency. For example, with 8-bit precision—offering two decimal places of accuracy—it requires only one-quarter of the memory and achieves 3–16 speedup compared to conventional floating-point-based method of computation. The proposed method is also compared with a traditional computation approach optimized using advanced Single-Instruction/Multiple-Data (SIMD) CPU operations, demonstrating its superior performance on modern architectures. • An algorithm for packing multiple truth values into a single integer value is presented. • Packing allows to compute multiple t-norm operations in parallel by performing bit operations. • The proposed approach results in less memory usage and greater efficiency of computation in areas such as association rules discovery.