Sequential Pattern Mining

8 papers with code • 0 benchmarks • 0 datasets

Sequential Pattern Mining is the process that discovers relevant patterns between data examples where the values are delivered in a sequence.

Source: Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities

Most implemented papers

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

kexinhuang12345/CASTER 15 Nov 2019

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.

A Subsequence Interleaving Model for Sequential Pattern Mining

mast-group/sequence-mining 16 Feb 2016

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database.

Constraint-based Sequential Pattern Mining with Decision Diagrams

aminhn/MPP 14 Nov 2018

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes.

Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union

rbun013/Rugby-Sequence-Data 29 Oct 2020

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.

Leveraging Language Foundation Models for Human Mobility Forecasting

cruiseresearchgroup/AuxMobLCast 11 Sep 2022

In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks.

HUSP-SP: Faster Utility Mining on Sequence Data

dsi-lab1/huspm 29 Dec 2022

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.

Mining compact high utility sequential patterns

clarkdinh/chusp 22 Feb 2023

To reduce complexity and obtain a compact set of frequent high utility sequential patterns (FHUSPs), this paper proposes an algorithm named CHUSP for mining closed frequent high utility sequential patterns (CHUSPs).

Causal Analysis of Customer Churn Using Deep Learning

DavidHason/Causal_Analysis International Conference on Digital Society and Intelligent Systems (DSInS) 2021

Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives.