Using diversity in cluster ensembles

By Ludmila I. Kuncheva, Stefan Todorov Hadjitodorov. IEEE International Conference on Systems, Man and Cybernetics, 2004

307 citations recorded by the source corpus.

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A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification (2014, methodology): “To explore the robustness of the nonlinear problem, simple doughnut data [34], [35], a simulated two-class dataset containing two true features and ten uniformly random noise features was generated, as shown in Fig.”
Detecting communities of triangles in complex networks using spectral optimization (2010, background): “In [33] the authors define the NMI to compare two clusterings.”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, background): “This induces diversity in the ensemble, and has been found to be one of the most useful cluster ensemble heuristics [12], [13], [16], [17], [21].”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, background): “The effect of diversity of models, and different ways of designing better ensembles making use of diverse components, has been studied extensively in the literature on ensemble clustering [43; 28; 27; 20; 6].”
Advances in Fuzzy Clustering and its Applications (2007, methodology): “They fi nd that the consensus function’s accuracy increases as the ensemble is made more diver se. Kuncheva and Hadjitodorov (2004) study the diversity of ensembles using multiple measures like the Rand Index, Jaccard measure etc.”