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The paper has proposed a linear unsupervised transfer learning (LUTL). Therefore, a cost function has been introduced. In the cost function of the proposed LUTL, the aim is to minimize the difference between the distribution of the transformed source domain (SD) data and the distribution of the target domain (TD) data. In the proposed cost function, it is also targeted to preserve the local structures of the untransformed SD data. Three mechanisms have been proposed for the preservation of local structures in the untransformed SD data: (1) minimization of the distances between the data pairs that are similar to each other in the untransformed SD data, (2) preservation of the clusters emerged in the untransformed SD data and finally (3) their combination. The optimization problem has emerged as a nonlinear one. Two techniques have been introduced to obtain an approximation of the optimal weight matrix. Each technique guarantees to reach a local optimum, but no one guarantees to reach the global solution. While the first method is an iterative one, the second is a relaxed version of the optimization problem. The paper shows also experimentally that the proposed techniques overshadow the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Clustering as a major task in data mining is responsible for discovering hidden patterns in unlabeled datasets. Finding the best clustering is also considered as one of the most challenging problems in data mining. Due to the problem complexity and the weaknesses of primary clustering algorithm, a large part of research has been directed toward ensemble clustering methods. Ensemble clustering aggregates a pool of base clusterings and produces an output clustering that is also named consensus clustering. The consensus clustering is usually better clustering than the output clusterings of the basic clustering algorithms. However, lack of quality in base clusterings makes their consensus clustering weak. In spite of some researches in selection of a subset of high quality base clusterings based on a clustering assessment metric, cluster-level selection has been always ignored. In this paper, a new clustering ensemble framework has been proposed based on cluster-level weighting. The certainty amount that the given ensemble has about a cluster is considered as the reliability of that cluster. The certainty amount that the given ensemble has about a cluster is computed by the accretion amount of that cluster by the ensemble. Then by selecting the best clusters and assigning a weight to each selected cluster based on its reliability, the final ensemble is created. After that, the paper proposes cluster-level weighting co-association matrix instead of traditional co-association matrix. Then, two consensus functions have been introduced and used for production of the consensus partition. The proposed framework completely overshadows the state-of-the-art clustering ensemble methods experimentally. [ABSTRACT FROM AUTHOR]