Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach

By Xiaoli Z. Fern, Carla E. Brodley. International Conference on Machine Learning, 2003

723 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7fe7a235a3a9f9cb3996653829d96a227b4b5754

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Citations in the corpus

Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, methodology): “, using similarity measures (again, as those available from external validity measures) or more refined methods of deriving some consensus partition [67; 19; 5; 69; 68; 21; 25; 55].”
Solving cluster ensemble problems by bipartite graph partitioning (2004, methodology): “Recall that the commonly used agglomerative approach (Fred & Jain, 2002; Fern & Brodley, 2003; Monti et al., 2003) generates a similarity matrix from the cluster ensemble and then performs agglomerative clustering using the similarity matrix.”
A survey on ensemble learning (2019, methodology): “Fern and Brodley [126] used the random projection for ensemble clustering and analyzed the impacts of qualities and diversities of individual clustering result on the final prediction.”
Rotation Forest: A New Classifier Ensemble Method (2006, methodology): “The new feature set was split randomly into three subsets.”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “Among these are random initializations of the clustering algorithm, subsampling, or resampling the data [5], [8], [9], [13], [16], [26], [27], applying different types of clustering algorithms [1], [16], [18], [37], using subsets of features [16], [32], “weakening” the clustering algorithm [16], [34”
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance (2010, methodology): “…purpose of clustering comparison, this class of measures has been popularized through the works of Strehl and Ghosh (2002) and Meil˘ a (2005), and since then has been employed in various subsequent research (Fern and Brodley, 2003; He et al., 2008; Asur et al., 2007; Tumer and Agogino, 2008).”
Clustering ensembles: models of consensus and weak partitions (2005, unknown): “Other related work include [7], [11], [16], [19], [28], [31], [35].”
Using diversity in cluster ensembles (2004, methodology): “The solution proposed in [4] is to use random linear projections in lower-dimensional spaces and run the clustering in these spaces.”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “(1) For the same data set, employ the same algorithm with the different parameters or the different initial conditions [85]; (2) For the same data set, employ the different algorithms [86]; (3) For the subsets, carry out the clustering respectively [86]; (4) For the same data set, carry out the clus”
Texture Classification from Random Features (2012, methodology): “Previously, random projection has been studied as a general dimensionality reduction method for numerous clustering problems [37], [38], [39], [43] as well as for learning nonlinear manifolds [42], [48].”
Subspace clustering for high dimensional data: a review (2004, methodology): “With such datasets, random searching becomes a viable heuristic method and has been used with many of the aforementioned criteria [1; 12; 29].”
Cluster ensembles (2011, background): “Fern and Brodley 14 also showed that clustering in high dimension is much more effective compared to clustering with principal component analysis (PCA) when the data points are randomly projected onto a sub-space, clustered in that subspace and consensus clustering is performed with this ensemble.”
Active Semi-Supervision for Pairwise Constrained Clustering (2004, background): “NMI has also become a popular clustering evaluation metric [2, 12, 15].”
Very sparse random projections (2006, methodology): “INTRODUCTION Random projections [1, 43] have been used in Machine Learning [2, 4, 5, 13, 14, 22], VLSI layout [42], analysis of Latent Semantic Indexing (LSI) [35], set intersections [7, 36], .nding motifs in bio-sequences [6, 27], face recognition [16], privacy preserving distributed data minin”