The classic sequential frequent pattern mining algorithms are based on a uniform mining support, either miss interesting patterns of low support or suffer from the bottleneck of pattern generation. In this thesis, we extend FP-growth to attack the problem of multi-level multi-dimensional sequential frequent pattern mining. The experimental result shows that our E-FP is more flexible at capturing desired knowledge than previous studies.
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