A Space Optimization for FP-Growth

Frequency mining problem comprises the core of several data mining algorithms. Among frequent pattern discovery algorithms, FP-GROWTH employs a unique search strategy using compact structures resulting in a high performance algorithm that requires only two database passes. We introduce an enhanced version of this algorithm called FP-GROWTH-TINY which can mine larger databases due to a space optimization eliminating the need for intermediate conditional pattern bases. We present the algorithms required for directly constructing a conditional FP-Tree in detail. The experiments demonstrate that our implementation has a running time performance comparable to the original algorithm while reducing memory use up to twofold.