A Reconfigurable Platform for Frequent Pattern Mining

In this paper, a new hardware architecture for frequent pattern mining based on a systolic tree structure is pro-posed. The goal of this architecture is to mimic the internal memory layout of the original FP-growth algorithm while achieving a much higher throughput. We also describe an embedded platform implementation of this architecture along with detailed analysis of area requirements and per-formance results for different configurations. Our results show that with an appropriate selection of tree size, the re-configurable platform can be several orders of magnitude faster than the FP-growth algorithm.

A novel MapReduce Lift association rule mining algorithm (MRLAR) for Big Data

Big Data mining is an analytic process used to dis-cover the hidden knowledge and patterns from a massive, com-plex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed inter-est in using association rule mining (ARM) in Big Data to uncov-er relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scal-able and parallel strategies for ARM based on Big Data ap-proaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the effi-ciency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.Web of Science7315715