We propose a new technique for clustering of text documents that relies on a biclustering structure constructed on terms and documents. Our approach makes use of a greedy algorith...
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
Abstract. We present a hybrid machine learning approach for information extraction from unstructured documents by integrating a learned classifier based on the Maximum Entropy Mod...
To date, higher educational organizations are placed in a very high competitive environment. To remain competitive, one approach is to tackle the student and administration challen...
A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive cl...