text: The emergence of high-dimensional data in various areas has brought new challenges to the ensemble clustering research. To deal with the curse of dimensionality, considerable efforts in ensemble clustering have been made by incorporating various subspace-based techniques. Besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large number of diversified metrics, and furthermore, how to jointly exploit the multi-level diversity in the large number of metrics, subspaces, and clusters, in a unified framework. To tackle this problem, this paper proposes a novel multi-diversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is adopted to explore the cluster-wise diversity in ensembles, based on which the consensus function is therefore presented. Experimental results on twenty high-dimensional datasets have confirmed the superiority of our approach over the state-of-the-art.
  @cite_4: Clustering ensembles have emerged as a powerful method for improving both the robustness as well as the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial, or statistical perspectives. This study extends previous research on clustering ensembles in several respects. First, we introduce a unified representation for multiple clusterings and formulate the corresponding categorical clustering problem. Second, we propose a probabilistic model of consensus using a finite mixture of multinomial distributions in a space of clusterings. A combined partition is found as a solution to the corresponding maximum-likelihood problem using the EM algorithm. Third, we define a new consensus function that is related to the classical intraclass variance criterion using the generalized mutual information definition. Finally, we demonstrate the efficacy of combining partitions generated by weak clustering algorithms that use data projections and random data splits. A simple explanatory model is offered for the behavior of combinations of such weak clustering components. Combination accuracy is analyzed as a function of several parameters that control the power and resolution of component partitions as well as the number of partitions. We also analyze clustering ensembles with incomplete information and the effect of missing cluster labels on the quality of overall consensus. Experimental results demonstrate the effectiveness of the proposed methods on several real-world data sets.
  @cite_0: Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.
  @cite_40: Translation with annotations of E. Weiszfeld, Sur le point pour lequel la somme des distances de n points donnes est minimum, Tohoku Mathematical Journal (first series), 43 (1937) pp. 355–386. A short introduction about the translation is found in Appendix A. Appendix B lists particular notations used by Weiszfeld and their now more conventional equivalents. Numbered footnotes are those of the original paper of Weiszfeld. Boxed numerals are references to observations about the translation and comments of the translator, all to be found in Appendix C. Copyright Springer Science+Business Media, LLC 2009
  @cite_15: In this paper, we propose a new ensemble clustering approach termed ensemble clustering using factor graph (ECFG). Compared to the existing approaches, our approach has three main advantages: (1) the cluster number is obtained automatically and need not to be specified in advance; (2) the reliability of each base clustering can be estimated in an unsupervised manner and exploited in the consensus process; (3) our approach is efficient for processing ensembles with large data sizes and large ensemble sizes. In this paper, we introduce the concept of super-object, which serves as a compact and adaptive representation for the ensemble data and significantly facilitates the computation. Through the probabilistic formulation, we cast the ensemble clustering problem into a binary linear programming (BLP) problem. The BLP problem is NP-hard. To solve this optimization problem, we propose an efficient solver based on factor graph. The constrained objective function is represented as a factor graph and the max-product belief propagation is utilized to generate the solution insensitive to initialization and converged to the neighborhood maximum. Extensive experiments are conducted on multiple real-world datasets, which demonstrate the effectiveness and efficiency of our approach against the state-of-the-art approaches. HighlightsIntroduce the super-object representation to facilitate the consensus process.Probabilistically formulate the ensemble clustering problem into a BLP problem.Propose an efficient solver for the BLP problem based on factor graph.The cluster number of the consensus clustering is estimated automatically.Our method achieves the state-of-the-art performance in effectiveness and efficiency.
  @cite_34: Ensemble clustering is a recently evolving research direction in cluster analysis and has found several different application domains. In this work the complex ensemble clustering problem is reduced to the well-known Euclidean median problem by clustering embedding in vector spaces. The Euclidean median problem is solved by the Weiszfeld algorithm and an inverse transformation maps the Euclidean median back into the clustering domain. In the experiment study different evaluation strategies are considered. The proposed embedding strategy is compared to several state-of-art ensemble clustering algorithms and demonstrates superior performance. HighlightsThis paper presents a novel approach to solving the ensemble clustering problem.An embedding method successful for strings and graphs is adapted to clusterings.An extensive experiment study is presented.The proposed method is superior to several state-of-the-art related algorithms.
summary: The median partition based methods @cite_15 @cite_4 @cite_34 typically formulate the ensemble clustering problem into an optimization problem which aims to find the median partition such that the similarity between the base partitions (i.e., base clusterings) and the median partition is maximized. The median partition problem is NP-hard @cite_4 . To find an approximate solution, @cite_4 cast the median partition problem into a maximum likelihood problem and solved it by the EM algorithm. Franek and Jiang @cite_34 reduced the ensemble clustering problem to an Euclidean median problem and solved it by the Weiszfeld algorithm @cite_40 . @cite_15 formulated the ensemble clustering problem into a binary linear programming problem and obtained an approximate solution based on the factor graph model and the max-product belief propagation @cite_0 .