K-means clustering is one of the most popular clustering algorithms used in data mining. However, clustering is a time consuming task, particularly with the large data sets found in data mining. In this paper we show how bootstrap averaging with k-means can produce results comparable to clustering all of the data but in much less time. The approach of bootstrap (sampling with replacement) avera...

Interval type-2 fuzzy sets, each of which is characterized by the footprint of uncertainty, are a very useful means to depict the linguistic information in the process of decision making. In this article, we investigate the group decision making problems in which all the linguistic information provided by the decision makers is expressed as interval type-2 fuzzy decision matrices where each of ...

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem ...

OBJECTIVE The present study presents a novel approach to averaging of event-related potentials (ERPs). Acknowledging latency variability of late ERP components as related to performance fluctuations across trials should improve the assessment of late portions of the ERP. METHODS Prior to the averaging procedure stimulus-to-response epochs in the electroencephalogram (EEG) were expanded/compre...