Subspace clustering for high dimensional data: a review

By Lance Parsons, Ehtesham Haque, Huan Liu. SKDD, 2004

1,528 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ed21943b6b505d8e04e8f03928585ded23a6983a

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Citations in the corpus

An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, methodology): “The subspace clustering methods in this category can be further divided into bottom-up and top-down subspace search methods [23].”
High-dimensional data clustering (2006, background): “They are usually based on grid search methods and find dense clusterab le subspaces [8].”
Feature Selection: A literature Review (2014, background): “In literature, many algorithms (subspace clustering) have been developed [126].”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “SUBSPACE clustering has been successfully used in numerous applications, especially those involving high-dimensional data [1], [2].”
Robust Subspace Clustering (2013, background): “In recent years, numerous algorithms have been developed for subspace clustering and applied to various problems in computer vision/machine learning [53] and data mining [43].”
Finding and Leveraging Structure in Learning Problems (2012, background): “Due to its practical importance and difficulty bi-clustering has attracted considerable attention (for some recent surveys see the papers [36, 134, 150, 184]).”
Toward integrating feature selection algorithms for classification and clustering (2005, neutral): “Many subspace clustering algorithms are developed [72].”
Finding large average submatrices in high dimensional data (2009, background): “The penalty depends separately on k and l, and its combinatorial form suggests a connection with the Minimum Description Length Principle (MDL), following Rissanen (2004), Grunwald (2004), and Barron and Yu (1998).”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, background): “As the number of dimensions in a dataset increases, distance measures become increasingly meaningless [16] .”
Subspace clustering (2012, background): “Two major issues can be distinguished in the clustering problem in general, namely (1) the adopted paradigm and algorithmic approach to clustering and (2) the definition and assessment of similarity versus dissimilarity.”
Experimental analysis of new algorithms for learning ternary classifiers (2015, background): “Learning classifiers in very high dimension data has received more and more attention in the past ten years both theoretically [2], [3], [4], [5], [6], [7] and in practice through Data Mining applications [8], [9], [10], [11], [12], [13], [14], [15], [16], [17].”
Model-based clustering of high-dimensional data: A review (2014, methodology): “A review on heuristic methods is given by [79].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “In the survey by Parsons et al. (2004) the problem is introduced in a very illustrative way and some approaches are sketched out.”