Clustering objects on subsets of attributes (with discussion)

By Jerome H. Friedman, Jacqueline J. Meulman. 2004

414 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0756b7c36e249dcce461b5b8e1266993d5321815

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Liu
2. Daniela M. Witten
3. Xiaotong Shen
4. Bettina Grün
5. Wei Pan
6. Robert Tibshirani
7. Lei Yu
8. Nema Dean

In Molecular Biology

1. Huan Liu
2. Daniela M. Witten
3. Charles M. Perou
4. Andrew B. Nobel
5. Robert Tibshirani
6. Xiaotong Shen
7. Wei Pan
8. Lei Yu

Citations in the corpus

• Finding large average submatrices in high dimensional data (2009, methodology): “…the literature, for example: the rows of U are approximately equal to each other [Aggarwal et al. (1999)]; the columns of U are approximately equal [Friedman and Meulman (2004)]; the elements of U are well-fit by a 2-way ANOVA model [Cheng and Church (2000); Lazzeroni and Owen (2002); Wang et al.…”
• A Framework for Feature Selection in Clustering (2010, methodology): “We used the criterion (7) to define K -means clustering, and consequently to derive a method for sparse K -means clustering, for simplicity and consistency with the COSA method of Friedman and Meulman (2004).”
• Toward integrating feature selection algorithms for classification and clustering (2005, background): “subspaces of very small dimensionality [ 32 ], either with their sets of dimensions overlapped or”
• Penalized Model-Based Clustering with Application to Variable Selection (2007, methodology): “Due to lack of statistical models in many existing clustering algorithms, it is difficult to implement principled variable selection, though some promising methods have been proposed, based largely on heuristics (Friedman and Meulman, 2004; Mangasarian and Wild, 2004).”
• An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, methodology): “Eight clustering algorithms, EWKM, FWKM [22], Bisecting k-means [36], LAC [19], PROCLUS [7], HARP [11], COSA [ 20 ], and SCAD1 [18], were tested on these data sets.”
• Challenging “Factor–Cluster Segmentation” (2008, methodology): “…understandable, it is nevertheless surprising to choose factor analysis for this purpose given that specific feature selection techniques have been developed for clustering and classification analysis to select a suitable subset of variables as segmentation base (Friedman and Meulman 2004).”
• Variable Selection for Clustering with Gaussian Mixture Models (2009, methodology): “The first one includes clustering methods with weighted variables (see, for instance, Friedman and Meulman, 2004) and dimension reduction methods.”
• Variable Selection for Model-Based Clustering (2006, methodology): “In the non–model-based clustering context, work on variable selection has been done by Dy and Brodley (2000), Lazzeroni and Owen (2002), Getz, Levine, and Domany (2000), McLachlan, Bean, and Peel (2002), McCallum, Nigam, and Ungar (2000), Brusco and Cradit (2001), Devaney and Ram (1997), Friedman…”