Finite mixture models have been used for unsupervised learning for over 60
years, and their use within the semi-supervised paradigm is becoming more
commonplace. Clickstream data is one of the various emerging data types that
demands particular attention because there is a notable paucity of statistical
learning approaches currently available. A mixture of first order continuous
time Markov models is introduced for unsupervised and semi-supervised learning
of clickstream data. This approach assumes continuous time, which distinguishes
it from existing mixture model-based approaches; practically, this allows
account to be taken of the amount of time each user spends on each website. The
approach is evaluated, and compared to the discrete time approach, using
simulated and real data.
Description
Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models
%0 Generic
%1 gallaugher2018clustering
%A Gallaugher, Michael P. B.
%A McNicholas, Paul D.
%D 2018
%K final thema:clickstream_clustering
%T Clustering and Semi-Supervised Classification for Clickstream Data via
Mixture Models
%U http://arxiv.org/abs/1802.04849
%X Finite mixture models have been used for unsupervised learning for over 60
years, and their use within the semi-supervised paradigm is becoming more
commonplace. Clickstream data is one of the various emerging data types that
demands particular attention because there is a notable paucity of statistical
learning approaches currently available. A mixture of first order continuous
time Markov models is introduced for unsupervised and semi-supervised learning
of clickstream data. This approach assumes continuous time, which distinguishes
it from existing mixture model-based approaches; practically, this allows
account to be taken of the amount of time each user spends on each website. The
approach is evaluated, and compared to the discrete time approach, using
simulated and real data.
@misc{gallaugher2018clustering,
abstract = {Finite mixture models have been used for unsupervised learning for over 60
years, and their use within the semi-supervised paradigm is becoming more
commonplace. Clickstream data is one of the various emerging data types that
demands particular attention because there is a notable paucity of statistical
learning approaches currently available. A mixture of first order continuous
time Markov models is introduced for unsupervised and semi-supervised learning
of clickstream data. This approach assumes continuous time, which distinguishes
it from existing mixture model-based approaches; practically, this allows
account to be taken of the amount of time each user spends on each website. The
approach is evaluated, and compared to the discrete time approach, using
simulated and real data.},
added-at = {2018-11-27T11:59:22.000+0100},
author = {Gallaugher, Michael P. B. and McNicholas, Paul D.},
biburl = {https://www.bibsonomy.org/bibtex/29c0702eba73384ceaf3f1b5c2f626f56/m.meissner},
description = {Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models},
interhash = {7b8427dcde591de476577fc99899a431},
intrahash = {9c0702eba73384ceaf3f1b5c2f626f56},
keywords = {final thema:clickstream_clustering},
note = {cite arxiv:1802.04849},
timestamp = {2018-11-27T11:59:22.000+0100},
title = {Clustering and Semi-Supervised Classification for Clickstream Data via
Mixture Models},
url = {http://arxiv.org/abs/1802.04849},
year = 2018
}