Clustering of check-in sequences using the mixture Markov chain process

TL;DR

This paper presents a novel clustering method for check-in sequences in geosocial networks using a mixture Markov chain model and EM algorithm, revealing detailed user communities in Weeplaces.

Contribution

It introduces a mixture Markov chain approach combined with EM for clustering time-dependent check-in data, a new application in geosocial network analysis.

Findings

Identified detailed user communities in Weeplaces

Demonstrated effectiveness of mixture Markov chains for clustering

Achieved high-resolution clustering of check-in sequences

Abstract

This work is devoted to the clustering of check-in sequences from a geosocial network. We used the mixture Markov chain process as a mathematical model for time-dependent types of data. For clustering, we adjusted the Expectation-Maximization (EM) algorithm. As a result, we obtained highly detailed communities (clusters) of users of the now defunct geosocial network, Weeplaces.