Subspace Clustering—A Survey

Kelkar, Bhagyashri A.; Rodd, Sunil F.

doi:10.1007/978-981-13-1402-5_16

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…PCA-based clustering algorithms use PCA to detect lowdimensional subspaces defined by correlations between attributes. Hough transform-based clustering algorithms do not rely on the locality assumption, and thus can be used for global subspace clustering [3,18,40].…”

Section: Main Findings 321 Subspace Clustering Process In High-dimens...mentioning

confidence: 99%

“…However, the explanations are brief, and no article explicitly focused on how to perform clustering in highdimensional data streams. Although [17][18][19][20] have published surveys on clustering high-dimensional data, these articles do not focus on data streams. Due to the lack of systematic reviews on clustering of high-dimensional data streams, this article conducts a literature review that focuses on clustering in high-dimensional data streams and fills the gaps of previous reviews.…”

Section: Introductionmentioning

confidence: 99%

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Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

Ghani¹,

Aziz²,

AbdulKadir³

2023

Computers, Materials &Amp; Continua

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Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature review on data stream clustering, there is currently no specific review on subspace clustering algorithms in high-dimensional data streams. Therefore, this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments. The review follows a systematic methodological approach and includes 18 articles for the final analysis. The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams. The main findings relate to six elements: clustering process, cluster search, subspace search, synopsis structure, cluster maintenance, and evaluation measures. Most algorithms use a two-phase clustering approach consisting of an initialization stage, a refinement stage, a cluster maintenance stage, and a final clustering stage. The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters. Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers. Future work can focus on the clustering framework, parameter optimization, subspace search techniques, memory-efficient synopsis structures, explicit cluster change detection, and intrinsic performance metrics. This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

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Section: Main Findings 321 Subspace Clustering Process In High-dimens...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

Ghani¹,

Aziz²,

AbdulKadir³

2023

Computers, Materials &Amp; Continua

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“…Examples of deep learning subspace clustering algorithms include StructAE, which uses deep neural networks [27], SSC, a graph structured autoencoder [28], and SDEC that performs semi-supervised deep embedded clustering [29]. Kelkar et al provide a more complete overview of the wider subspace clustering field in a recent survey [30].…”

Section: Related Workmentioning

confidence: 99%

RASCL: a randomised approach to subspace clusters

Moens

Čule

Goethals

2022

Int J Data Sci Anal

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Subspace clustering aims to discover clusters in projections of highly dimensional numerical data. In this paper, we focus on discovering small collections of highly interesting subspace clusters that do not try to cluster all data points, leaving noisy data points unclustered. To this end, we propose a randomised method that first converts the highly dimensional database to a binarised one using projected samples of the original database. Subsequently, this database is mined for frequent itemsets, which we show can be translated back to subspace clusters. In this way, we are able to explore multiple subspaces of different sizes at the same time. In our extensive experimental analysis, we show on synthetic as well as real-world data that our method is capable of discovering highly interesting subspace clusters efficiently.

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“…The present work explores the effect of distance concentration on clustering in highdimensional spaces. There has been substantial research into the development of "dimensionaltolerant" algorithms [6,12,20,51,64,74], including subspace and projective methods [3,23,41,42,49,52] and modifications to classic clustering algorithms [14,34,71]. These methods are intended to outperform conventional algorithms like k-means (and other Euclidean distance-based methods) when the number of features becomes large.…”

Section: Introductionmentioning

confidence: 99%

How I learned to stop worrying and love the curse of dimensionality: an appraisal of cluster validation in high-dimensional spaces

Powell¹

2022

Preprint

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The failure of the Euclidean norm to reliably distinguish between nearby and distant points in high dimensional space is well-known. This phenomenon of distance concentration manifests in a variety of data distributions, with iid or correlated features, including centrally-distributed and clustered data. Unsupervised learning based on Euclidean nearest-neighbors and more general proximity-oriented data mining tasks like clustering, might therefore be adversely affected by distance concentration for high-dimensional applications. While considerable work has been done developing clustering algorithms with reliable high-dimensional performance, the problem of cluster validation-of determining the natural number of clusters in a dataset-has not been carefully examined in high-dimensional problems. In this work we investigate how the sensitivities of common Euclidean norm-based cluster validity indices scale with dimension for a variety of synthetic data schemes, including well-separated and noisy clusters, and find that the overwhelming majority of indices have improved or stable sensitivity in high dimensions. The curse of dimensionality is therefore dispelled for this class of fairly generic data schemes.

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Subspace Clustering—A Survey

Cited by 7 publications

References 23 publications

Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

RASCL: a randomised approach to subspace clusters

How I learned to stop worrying and love the curse of dimensionality: an appraisal of cluster validation in high-dimensional spaces

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