RASP: Robust Mining of Frequent Temporal Sequential Patterns under Temporal Variations

This repository contains the source code for the paper RASP: Robust Mining of Frequent Temporal Sequential Patterns under Temporal Variations.

In this work, we propose RASP, an algorithm for Robust and resource-Adaptive mining of temporal Sequential Patterns. RASP is built upon the following ideas, each devised to address the above limitations:

• Relaxed TSPs and Duplicated Pattern Matching: For robustness against temporal variation, RASP enables multiple TSPs to share the same instance based on the novel concept of a relaxed TSP, which permits a predefined level of time gap deviation.
• Resource-Adaptive Automatic Hyperparameter Tuning: RASP gradually increases the sizes of TSPs to detect larger TSPs. In order to maintain a proper number of TSPs of each size, \method adaptively adjusts thresholds based on the available resources, enhancing its usability.
• Tree-based Concise Data Structure: RASP employs a tree-based compact data structure to efficiently manage the increasing number of TSPs, improving both speed and space efficiency.

Datasets

All datasets are available at this link.

Experiment	Dataset	Event	Source
Main	Neuron Activity	Spike of a Neuron	CN2 Simulator
Additional	E-Commerce	Click on a product	YOOCHOOSE Gmbh

Requirements

To install requirements, run the following command on your terminal:

pip install -r requirements.txt

RASP on Neuron Activity Datasets

To execute RASP on neuron activity datasets, run this command:

./run.sh

RASP on an E-commerce Dataset

To execute RASP on an e-commerce dataset, run this command:

./run_case.sh

Evaluation

To evaluate the result TSPs, run this command:

python main.py -a read_ndcg_rc_exp

Reference

This code is free and open source for only academic/research purposes (non-commercial). If you use this code as part of any published research, please acknowledge the following paper.