In this paper we present a parameter optimisation procedure that is designed to automatically initialise the number of clusters and the initial colour prototypes required by data ...
The typical task of unsupervised learning is to organize data, for example into clusters, typically disjoint clusters (eg. the K-means algorithm). One would expect (for example) a...
Mark K. Goldberg, Mykola Hayvanovych, Malik Magdon...
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
An unsupervised part-of-speech (POS) tagging system that relies on graph clustering methods is described. Unlike in current state-of-the-art approaches, the kind and number of dif...
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are in...