Advances in imaging techniques have led to large repositories of images. There is an increasing demand for automated systems that can analyze complex medical images and extract me...
Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative m...
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transact...
Alexandre V. Evfimievski, Ramakrishnan Srikant, Ra...
When automatically extracting information from the world wide web, most established methods focus on spotting single HTMLdocuments. However, the problem of spotting complete web s...
Martin Ester, Hans-Peter Kriegel, Matthias Schuber...
A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software syst...
Mohammad El-Ramly, Eleni Stroulia, Paul G. Sorenso...
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Recently there has been an increasing interest in developing regression models for large datasets that are both accurate and easy to interpret. Regressors that have these properti...
In this paper we propose a new information-theoretic divisive algorithm for word clustering applied to text classification. In previous work, such "distributional clustering&...
Inderjit S. Dhillon, Subramanyam Mallela, Rahul Ku...
In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is mo...