Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering

By Hans-Peter Kriegel, Peer Kröger, Arthur Zimek. TKDD, 2009

1,255 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bc20444f09186991b2b5f05a174c7aac4ec1dd25

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

In Information Systems

In Signal Processing

Citations in the corpus

Contextual Outlier Interpretation (2017, background): “Varios approaches have been proposed to tackle the challenges including the curse of dimensionality [2, 16, 24], the massive data volumn [4, 40], and heterogenous information sources [17, 36].”
Subspace clustering (2012, background): “75 Such problems common to all of these areas are (1) how to treat diversity of clustering solutions (are diverse clustering solutions to be unified or to be presented individually?); (2) how to effectively summarize and present diversity to a user of clustering algorithms; (3) how to treat redundan”
Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data (2016, background): “…the performance of clustering algorithms in low-dimensional settings is in general not a good guide to performance in higher-dimensional settings (12,13); both clustering accuracy and computational efficiency may be severely affected, depending on the mathematical properties of the algorithm.”
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data (2009, background): “Second, we may have still concrete mechanisms that have generated the data but, usually, for each of these generating mechanisms only a subset of features may be relevant (this problem is known as local feature relevance [ 3 ]).”
Density-Based Projected Clustering of Data Streams (2012, background): “According to [16], one can differentiate between two main classes of subspace clustering algorithms that deal with static data:”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “Clustering methods such as the K-means algorithm, soft clustering based on mixed models, hierarchical clustering, and spectral clustering have been developed and have proven to be successful in many real world applications (Kriegel et al. 2009; Luxburg 2007; Steinbach et al. 2003; Parsons et al. 200”
Density‐based clustering (2019, background): “…fill only subspaces or manifolds of the embedding data space has motivated specialized subspace clustering methods that look for clusters in (typically linear) subspaces of the data space, where different clusters can reside in different subspaces (Kriegel et al., 2009, 2012; Zimek, 2013, chap.”
A Comprehensive Survey of Clustering Algorithms (2015, background): “The main basic ideas of clustering for big data can be summarized in the following 4 categories: (1) sample clustering [10,18]; (2) data merged clustering [17,134]; (3) dimension-reducing clustering [135,136]; (4) parallel clustering [114,137–139]; Typical algorithms of this kind of clustering areK-”
Optimized Cartesian K-Means (2014, neutral): “C V ] 16 M ay 2 01 4 Index Terms—Clustering, Cartesian product, Nearest neighbor search ✦”
A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “B, K-means is a hardassignment limit of the Gaussian mixture model where all cluster variances are assumed to be the same.”