A Mixture Model for Clustering Ensembles

By Alexander P. Topchy, Anil K. Jain, William F. Punch III. SDM, 2004

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/62d49d6fe57dbf6a020f1036b6986e0954daaf1b

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Citations in the corpus

Cluster ensembles (2011, methodology): “This immediately yields the pioneering approach taken in Ref 18.”
Advances in Fuzzy Clustering and its Applications (2007, methodology): “ Topchy et al. (2004) model the ensemble,�(1,2,...,r), using a generative model and use EM to”
A Survey of Evolutionary Algorithms for Clustering (2009, background): “of a consensus function to combine these partitions [126].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “means [103] and mixture models [105], for solving network planning problems, such as cluster-forming in wireless sensor networks (WSNs) [104] and small-cell deployment [132].”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “Table 16 Consensus functions Name Typical algorithm and application Based on co-association matrix [85] Based on graph partition CSPA, HGPA and MCLA [86] Based on relabeling and voting VM [88] Based on the hybrid model [89] Based on information theory [90] Based on genetic algorithm HCE [91] Based o”
Ensemble learning: A survey (2018, methodology): “The integration of multiple clustering models into a consensus clustering is considered the hardest part in creating a clustering ensemble (Topchy, Jain, & Punch, 2004).”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “; L) [9], [32], [37], the feature-based approach (treating outputs from the clusterers as L categorical features and building a clusterer thereupon) [35], the hyper-graph approach (constructing a hyper-graph representing the total output from the clusterers and cutting the redundant edges) [32], and”
An Evolutionary Approach to Multiobjective Clustering (2007, methodology): “algorithm) can be alleviated through the application and comparison of multiple clustering methods [8], or through the a posteriori combination of different clustering results by means of ensemble methods [9], [10].”
Solving cluster ensemble problems by bipartite graph partitioning (2004, methodology): “Recently Topchy et al. (2003; 2004) propose to represent a cluster ensemble as a new set of features describing the instances and produce ﬁnal clusters by applying K - means and EM to the new features.”