We propose a hybrid, unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization. We developed several heuristics to ...
Hierarchical clustering methods are important in many data mining and pattern recognition tasks. In this paper we present an efficient coarse grained parallel algorithm for Single...
In this paper, we explore the discriminating subsequencebased clustering problem. First, several effective optimization techniques are proposed to accelerate the sequence mining p...
Jianyong Wang, Yuzhou Zhang, Lizhu Zhou, George Ka...
In this paper, we address the peak detection and alignment problem in the analysis of mass spectrometry data. To deal with the peak redundancy problem existing in the MALDI data a...
Weichuan Yu, Baolin Wu, Ning Lin, Kathy Stone, Ken...
In this paper we propose an encoding scheme and ad hoc operators for a genetic approach to hierarchical graph clustering. Given a connected graph whose vertices correspond to poin...