Ensemble clustering in medical diagnostics

Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, analogous techniques for cluster analysis have been suggested. Research has demonstrated that, by combining a collection of dissimilar clusterings, an improved solution can be obtained. In this paper, we examine the potential of applying ensemble clustering techniques with a focus on the area of medical diagnostics. We present several ensemble generation and integration strategies, and evaluate each approach on a number of synthetic and real-world datasets. In addition, we show that diversity among ensemble members is necessary, but not sufficient to yield an improved solution without the selection of an appropriate integration method.