As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical clusters, while we are interested in clusters of arbitrary sizes and shapes. The novelty of our approach relies on the use of a k-nearest neighbor graph to determine candidate constraints coupled with a new constraint utility function. Comparative experiments conducted on real datasets from machine learning repository show that our approach significantly improves the results of constraints based clustering algorithms.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.