Clustering of check-in sequences using the mixture Markov chain process
This work addresses clustering of user data in geosocial networks, but it appears incremental as it applies an existing method to a specific dataset.
The authors tackled the problem of clustering check-in sequences from a geosocial network by using a mixture Markov chain process and adjusting the Expectation-Maximization algorithm, resulting in highly detailed user communities from the Weeplaces dataset.
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.