Semisupervised Clustering for Intelligent User Management
(February 2004)Abstract
Grouping users automatically based on their system usage
can be beneficial in an autonomic computing environment.
Clustering algorithms can generate meaningful
user groups that provide important insights to system administrators
about user profiles and group policies. In
particular, if a small amount of supervision is provided by
the administrator to the clustering process, semi-supervised
clustering algorithms can use this supervision to generate
clusters which are more useful for user management.
In this work, we demonstrate the utility of semisupervised
clustering in intelligent user management. We
collect publicly available system usage data of users in
a university computing environment, and cluster the users
using semi-supervised hierarchical agglomerative clustering
based on the profile of the processes they run. Initial
supervision is provided in the form of a few users running
a specific process. Semi-supervised clustering gives
us more meaningful clusters than unsupervised clustering
in this domain, demonstrating that our technique can find
interesting and useful groups in data with minimal user
intervention.