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 ...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.展开更多
文摘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.
