Extraction of Frequent Sub-Sequences from Long Sequence Using Neural Network

Abstract

In this paper, we aim to extract frequent sub-sequences from a given long sequence. Especially, the method satisfies following four requirements: (1) online learning, (2) extracting plural sub-sequences, (3) extracting various length sub-sequences, and (4) controlling threshold related to frequency. The proposed method uses a 2-blocks neural network. The network consists of spiking neurons based on leaky integrate and fire (LIF) model and is trained by the method based on spike timing dependency plasticity (STDP). As a result, the network extracted sub-sequences whose frequency is more than a certain threshold that is determined by only one parameter. Concretely, the network extracted three-symbols-length subsequences from 3,000 length sequence. In this case, sub-sequences appeared frequency of 0.4%, 3%, or 5%, and the network extracted 3%-and-more sub-sequences, or only 5% sub-sequences by controlling only one parameter.