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
Benchmarks
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Latest papers
Causal Analysis of Customer Churn Using Deep Learning
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.
Mining compact high utility sequential patterns
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).
HUSP-SP: Faster Utility Mining on Sequence Data
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
Leveraging Language Foundation Models for Human Mobility Forecasting
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.
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
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.
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Constraint-based Sequential Pattern Mining with Decision Diagrams
Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes.
A Subsequence Interleaving Model for Sequential Pattern Mining
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.