Active Constraint Clustering by Instance-level Constraint Ranking Using Estimated Cluster Boundaries, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Taking into account the fast and ever-increasing pace of data growth, clustering algorithms emerge as the key tools for data analysis in new researches. Clustering remain as a method for decomposing data into clusters, in such a way that similar data coalesce in the same group. Different algorithms conduct clustering according to a series of initial hypotheses, without being informed about the clusters’ form and aims. Hence, in case with no conformity between initial hypothesis and the clustering aim, one cannot expect adequate response from the clustering algorithm. Exploitation of side information in clustering can play an impactful role in introduction of real models into clustering...
Cataloging briefActive Constraint Clustering by Instance-level Constraint Ranking Using Estimated Cluster Boundaries, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Taking into account the fast and ever-increasing pace of data growth, clustering algorithms emerge as the key tools for data analysis in new researches. Clustering remain as a method for decomposing data into clusters, in such a way that similar data coalesce in the same group. Different algorithms conduct clustering according to a series of initial hypotheses, without being informed about the clusters’ form and aims. Hence, in case with no conformity between initial hypothesis and the clustering aim, one cannot expect adequate response from the clustering algorithm. Exploitation of side information in clustering can play an impactful role in introduction of real models into clustering...
Find in contentBookmark |
|