Efficient Sequential Pattern Discovery with PrefixSpan for Large-Scale Data Analytics

Abstract

The exponential growth of big data has intensified the demand for scalable association rule mining techniques capable of uncovering meaningful sequential patterns. This study evaluates the PrefixSpan algorithm, which recursively constructs frequent patterns from prefixes, thereby reducing the search space and enhancing efficiency in large-scale applications. Experimental analysis demonstrates its capability to discover frequent itemset and sequential dependencies across synthetic datasets, with visualization matrices highlighting strong associations and recurring sequences. The algorithm achieves significant efficiency gains by focusing on relevant postfixes, enabling practical use in domains such as market basket analysis, bioinformatics, and social network analysis. Results further indicate that PrefixSpan effectively balances computational performance and scalability, supporting incremental updates for dynamic environments while minimizing memory overhead. Comparative analysis with alternative approaches shows PrefixSpan’s superior adaptability and reduced computational demands, though challenges remain in handling extremely large datasets and non-contiguous pattern discovery. Quantitative evaluation demonstrates that PrefixSpan identifies frequent itemset with high occurrence counts, extracts sequential dependencies across multiple sequences, and achieves measurable efficiency improvements in scalability and reduced search space compared to Apriori and SPADE, while facing performance trade-offs in extremely large-scale data processing.