Data clustering: 50 years beyond K-means

By Anil K. Jain. 2008

9,525 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

Three Approaches for Personalization with Applications to Federated Learning (2020, background): “Clustering is a classical problem with a broad literature and known algorithms (Jain, 2010).”
Proportionally Fair Clustering (2019, background): “, 2004), and is perhaps best known as the setting for the celebrated k-means heuristic (independently discovered many times, see (Jain, 2010) for a brief history).”
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm (2012, methodology): “As a result, numerous clustering algorithms have been proposed since the early 1950s [2].”
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, methodology): “One very popular partitioning method is the K-means clustering algorithm [18,24] .”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “, all variables have about the same variance within clusters [21], which means that it appears appropriate that variables that vary strongly within “true” clusters are weighted down.”
The Role of Big Data and Predictive Analytics in Retailing (2017, methodology): “…some form of cluster analysis (e.g., the k-means) has been used to group together units of analysis that share similarities along important variables (called basis variables), based on some inter-unit distance function in basis variable space (see Jain 2010 for a review of clustering methods).”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “The process continues iteratively until no modifications to the clusters can be made [154,155].”
Robust continuous clustering (2017, methodology): “Despite these developments, no single algorithm has emerged to displace the k -means scheme and its variants (21).”
CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013, methodology): “13 flowPeakssvm and Kmeanssvm Introduction: K-means algorithm was proposed more than 50 years ago and is still one of the most widely used algorithms for clustering [39].”
A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm (2020, neutral): “As the oldest visual method for estimating the potential optimal cluster number for the analyzed dataset, the Elbow method [11; 12] usually needs to perform the K-means on the same dataset with a contiguous cluster number range:[1, L] (L is an integer greater than 1).”
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, background): “Moreover, the underlying concept of K-centers is to find the smallest radius such that the entire data set is contained within k (the given number of selected bands) balls of the radius, indicating that the method is not able to detect nonspherical clusters [47], which is just the case of the hypers”
YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving (2021, methodology): “The YOLOv4 algorithm based on the K -means clustering [14] was used to cluster the bbox label information of the training data set and nine anchors were obtained in the experimental data set BDD100K.”
Compressive Spectral Clustering (2016, background): “Other authors, inspired by research aiming at reducing k-means complexity (Jain, 2010), such as the line of work on coresets (Har-Peled & Mazumdar, 2004), have proposed to circumvent k -means in high dimension by subsampling a few data points out of the N available ones, applying SC on its reduced…”