A small-system grand canonical ensemble Monte Carlo method is developed to evaluate cluster size distribution and barrier to the nucleation in a supersaturated Lennard-Jones vapor. The theoretical foundation is a physical cluster theory in which the Stillinger cluster is used as a prototypical physical cluster. Using method of Mayer’s cluster expansion, the cluster–vapor interaction is effectiv...

Cluster ensemble has been shown to be an effective thought of improving the accuracy and stability of single clustering algorithms. It consists of generating a set of partition results from a same data set and combining them into a final one. In this paper, we develop a novel cluster ensemble method named Cluster Ensemble algorithm using the Binary k-means and Spectral Clustering (CEBKSC). By u...

Voting-based consensus clustering is a subset of techniques that makes clear the cluster label mismatch issue. Finding best relabeling for given partition in relation to reference known as voting problem. As weighted bipartite matching problem, it frequently formulated. We propose more generic formulation problem regression with various and multiple-input variables this work. demonstrate how re...

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...

We present a cluster-based sampling and ensemble method to learn from large, imbalanced data set for bleeding detection in CE videos. Our method selects training examples randomly according to the data distributions derived from clustering. Multiple training sets are created such that data balance is restored. The sampling probability is proportional to the cluster distribution, and within each...

We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtained from component clustering methods by using the group-norm regularization. Moreover, the proposed method is...

Ensemble Learning refers to the procedures employed to train multiple learning machines and combine their outputs, treating them as a “committee” of decision makers. The principle is that the committee decision, with individual predictions combined appropriately, should have better overall accuracy, on average, than any individual committee member. Numerous empirical and theoretical studies hav...