As modern data sets continue to grow exponentially in size, the demand for efficient mining algorithms capable of handling such large data sets becomes increasingly imperative. This paper develops a memory-efficient approach for Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck for large data sets. Our methodology involves a novel hybrid trie data structure that exploits recurring patterns to compactly store the data set in memory; and a corresponding mining algorithm designed to effectively extract patterns from this compact representation. Numerical results on real-life test instances show an average improvement of 88% in memory consumption and 41% in computation time for small to medium-sized data sets compared to the state of the art. Furthermore, our algorithm stands out as the only capable SPM approach for large data sets within 256GB of system memory.
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