C4.5: programs for machine learning
C4.5: programs for machine learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cluster Analysis
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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High-throughput techniques have become a primary approach to gathering biological data. These data can be used to explore relationships between genes and guide development of drugs and other research. However, the deluge of data contains an overwhelming amount of unknown information about the organism under study. Therefore, clustering is a common first step in the exploratory analysis of high-throughput biological data. We present a supervised learning approach to clustering that utilises known gene-gene interaction data to improve results for already commonly used clustering techniques. The approach creates an ensemble similarity measure that can be used as input to any clustering technique and provides results with increased biological significance while not altering the clustering method.