Ensemble clustering generates data partitions by using different data representations and/or clustering algorithms. Each partition provides independent evidence to generate the final partition: two instances falling in the same cluster provide evidence towards them belonging to the same final partition.
In this paper we argue that, for some data representations, the fact that two instances fall in the same cluster of a given partition could provide little to no evidence towards them belonging to the same final partition. However, the fact that they fall in different clusters could provide strong negative evidence of them belonging to the same partition.
Based on this concept, we have developed a new ensemble clustering algorithm which has been applied to the heartbeat clustering problem. By taking advantage of the negative evidence we have decreased the misclassification rate over the MIT-BIH database, the gold standard test for this problem, from 2.25% to 1.45%.
Keywords: Clustering ensembles, Evidence accumulation, Heartbeat clustering, Heartbeat representation, Hermite functions, ECG