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In order to improve the performance of a clustering on a data set, a number of primary partitions are generated and stored in an ensemble and their aggregated consensus partition is used as their clustering. It is widely accepted that the consensus partition outperforms the primary partitions. In this paper, an ensemble clustering method called multi-level consensus clustering (MLCC) is proposed. To construct the MLCC, a cluster–cluster similarity matrix which is achieved by an innovative similarity metric is first generated. The mentioned cluster–cluster similarity matrix is based on a multi-level similarity metric. In fact, it can be computed in a new defined multi-level space. Then, a point–point similarity matrix which is boosted using the mentioned cluster–cluster similarity matrix is generated. The new consensus function applies an average linkage hierarchical clusterer algorithm on the mentioned point–point similarity matrix to make consensus partition. MLCC is better than traditional clustering ensembles and simple versions of clustering ensembles on traditional cluster–cluster similarity matrix. Its computational cost is not very bad too. Accuracy and robustness of the proposed method are compared with those of the modern clustering algorithms through the experimental tests. Also, time analysis is presented in the experimental results. [ABSTRACT FROM AUTHOR]

Model checking is one of the most efficient techniques in software system verification. However, state space explosion is a big challenge while using this technique to check different properties like safety ones. In this situation, one can search the state space to find a reachable state in which the safety property is violated. Hence, reachability checking can be done instead of checking safety property. However, checking reachability in the worst case may cause state space explosion again. To handle this problem, our idea is based on generating a small model consistent with the main model. Then by exploring the state space entirely, we search it to find the goal states. After finding the goal states, we label the paths which start from the initial state and leading to a goal state. Then using the ensemble classification technique, the necessary knowledge is extracted from these paths to intelligently explore the state space of the bigger model. Ensemble machine learning technique uses Boosting method along with decision trees. It follows sampling techniques by replacement. This method generates k predictive models after sampling k times. Finally, it uses a voting mechanism to predict the labels of the final path. Our proposed approach is implemented in GROOVE, which is an open source toolset for designing and model checking graph transformation systems. Our experiments show a significant improvement in terms of both speed and memory usage. In average, our approach consumes nearly 42% fewer memory than other approaches. Also, it generates witnesses nearly 20% shorter than others, in average. [ABSTRACT FROM AUTHOR]