Parallel Text Mining in Multicore Systems Using FP-tree Algorithm

Abstract

Frequent keyword mining (FKM) is a useful tool for discovering frequently occurring keywords in data. Many algorithms have been developed to speed up mining performance on single core systems. Unfortunately, when the dataset size is huge, both the memory use and computational cost can still be extremely expensive. In our paper, we try to parallelize the FP-Growth algorithm on multicore machines. We partition the huge database, into the number of cores, and utilize the combined strength of all the cores, to achieve maximum performance. We propose to use the generated FP Tree and its rules for the Trend analysis of news data.