Efficient partial multiple periodic patterns mining without redundant rules

Abstract

Partial periodic patterns mining is a very interesting domain in data mining problem. In the previous studies, full and partial multiple periodic patterns mining problems are considered. The proposed methods, however, may produce redundant information and are inefficient. In this paper, a novel concept and new parameters are proposed to improve the performance of partial multiple periodic patterns mining. Instead of considering the whole database, the information needed for mining partial periodic patterns is transformed into a bit vector which can be stored in a main memory. A set of simulations is also performed to show the benefit of our approach.