A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

By Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufou, Abdelaziz Bouras. IEEE Transactions on Emerging Topics in Computing, 2014

1,003 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/f34769765f75f94dc81b21faa2e50594ef1fcbfb

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Citations in the corpus

Mode matching in GANs through latent space learning and inversion (2018, methodology): “In order to quantitatively compare the performance of the methods considered, we use three metrics namely, clustering accuracy (ACC), normalized mutual information (NMI) and adjusted rand index (ARI), on balanced test data, with respect to the latent space reconstruction network [11].”
Search Result Clustering in Collaborative Sound Collections (2020, background): “Among the different approaches for clustering [12, 44], in the context of multimedia documents, density-based algorithms such as graph-based clustering methods are particularly well designed for dealing with computational efficiency and the heterogeneous aspect of the data [32].”
A survey towards an integration of big data analytics to big insights for value-creation (2018, methodology): “(Fahad et al., 2014) described few efficient algorithms such as sampling, data condensation approaches, density-based approach, grid-based approach, divide and conquer, incremental learning and distributed computing. (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) presented the steps that composed with k”
Digital twin paradigm: A systematic literature review (2021, background): “The unsupervised learning, instead, discovers an internal representation from input data only (Sutharssan et al., 2015), (Fahad et al., 2014).”
Big Data Analytics in Operations Management (2018, methodology): “It can be used in areas such as market segmentation, collaborative processes (Fan et al. 2017), classiﬁcation, clustering, (Fahad et al. 2014) and regression.”
Data-centric Artificial Intelligence: A Survey (2023, positive): “Furthermore, automated clustering methods [67] are frequently combined with dimensionality reduction techniques to organize data points in a grouped, categorized, and often color-coded fashion, facilitating human comprehension and insightful analysis of the data.”
A Taxonomy of Knowledge Gaps for Wikimedia Projects (First Draft) (2020, methodology): “for emotions, clustering algorithms, or educational objectives) [16, 33, 156], we will use this term to define the final product of our work throughout the manuscript.”
Multi-view clustering: A survey (2018, neutral): “Clustering has emerged as a powerful alternative learning tool for exploring the underlying structure of data[5, 6], especially in the era of big data[7].”
Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner (2016, positive): “This is a problem because many if not most clustering algorithms are sensitive to data size, variability and dimensionality (Fahad et al., 2014).”