Clustering Ensemble Algorithm Research Articles

A method for extracting interference striations in lofargram based on decomposition and clustering

AbstractIn complex underwater noise environment, target detection, recognition, and tracking are proceeded through the frequency spectrum analysis of the signals received by sonar systems, in which the lofargram plays a major role. Typically, if the distance of the target experiences far‐near‐far variation, there will be parabolic interference striations presented in the lofargram. However, the existing striations extraction methods sometimes lack objectivity and fail to extract the wide striations accurately. In this paper, a novel method for wide type interference striations extraction is developed based on efficient decomposition and ensemble clustering. To obtain valuable information, the lofargram is decomposed into smooth background, striations area, and noise, then a multi‐phase ensemble clustering algorithm is employed to extract parabolas from the decomposed striations area pixels. Experimental results on simulated and real‐life datasets exhibit the effectiveness of the proposed method, and verify that it has comparable performance with prevalent Hough transform while extracting striations.

ScBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.

Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. Supplementary data are available at Bioinformatics online.

The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data.

The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research. In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis. The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.

Parallel progressive-based inductive subspace and fuzzy-based firefly algorithm for high ensemble data clustering

Recently, the framework is proposed with techniques such as random subspace and constraint propagation for handling high dimensional data ensemble clustering. Huge dataset clustering is difficult for conventional sequential clustering methods since it requires higher computation time. Distributed parallel processing and methods are consequently useful towards attaining results and scalability constraints of clustering huge datasets. So, in this work, the parallel progressive-based inductive subspace ensemble clustering (PPISEC) algorithm is introduced with the concept of MapReduce (MR) to perform high dimensional data clustering. Depending on micro-clusters and correspondence relative, the clustering method is designed which is easily parallelised via MR and completed in moderately with a small number of MR rounds. However, in the PPISEC algorithm, the centroid values are selected with the help of an improved support vector machine (ISVM) classifier. Thus, the incremental ensemble member chosen (IEMC) progression is performed with fuzzy-based firefly algorithm (FFA), and the normalised cut algorithm is established to accomplish high dimensional data clustering. The outcome shows that, the proposed PPISEC framework, which performs well on three benchmark samples by utilising high dimensionality and enhanced the results than the conventional clustering ensemble approaches.

A Clustering Ensemble Method for Cell Type Detection by Multiobjective Particle Optimization.

Single-cell RNA sequencing (scRNA-seq) is a new technology different from previous sequencing methods that measure the average expression level for each gene across a large population of cells. Thus, new computational methods are required to reveal cell types among cell populations. We present a clustering ensemble algorithm using optimized multiobjective particle (CEMP). It is featured with several mechanisms: 1) A multi-subspace projection method for mapping the original data to low-dimensional subspaces is applied in order to detect complex data structure at both gene level and sample level. 2) The basic partition module in different subspaces is utilized to generate clustering solutions. 3) A transforming representation between clusters and particles is used to bridge the gap between the discrete clustering ensemble optimization problem and the continuous multiobjective optimization algorithm. 4) We propose a clustering ensemble optimization. To guide the multiobjective ensemble optimization process, three cluster metrics are embedded into CEMP as objective functions in which the final clustering will be dynamically evaluated. Experiments on 9 real scRNA-seq datasets indicated that CEMP had superior performance over several other clustering algorithms in clustering accuracy and robustness. The case study conducted on mouse neuronal cells identified main cell types and cell subtypes successfully.

A multi-view ensemble clustering approach using joint affinity matrix

In multi-view ensemble clustering, the correctly-partitioned data objects should be assigned with a higher weight, thereby helping to decrease the influence of incorrectly-partitioned data objects. Therefore, different data objects should be treated separately instead of being set the same view weight as traditional solutions. In this paper, a multi-view ensemble clustering approach is proposed using joint affinity matrix, which is generated by sample-level weight. Firstly, a new concept of core data objects is defined according to the influence index and Gaussian Mixed Model, and basic partitions and sample-level weights can be yielded for every view. Secondly, a joint affinity matrix, which maintains pairwise similarities of all data objects, is generated using the sample-level weights. Consequently, data objects can be effectively assigned to the correct partition. Thirdly, a multi-view ensemble clustering algorithm is proposed using the joint affinity matrix. In the end, experimental results on benchmark datasets validate the efficacy of the algorithm with state-of-the-art baselines.

Semi-Supervised Clustering Ensemble Based on Cluster Consensus Selection

Ensemble clustering emerged as an important extension of classical clustering problems and is one of the most recent advances in unsupervised learning. Its purpose is to combine the results obtained using different algorithms by a consensus function so that the final solution is more favorable than the individual clustering algorithms. In this study, we propose a semi-supervised clustering ensemble framework using cluster consensus selection, which tries to improve the accuracy of clustering results. In general, there are two types of semi-supervised clustering algorithms, including constraint-based and metric-based. Here, the proposed ensemble clustering algorithm is equipped with a semi-supervised clustering mechanism based on pairwise constraints. Since the complexity of consensus functions scales with the number of clustering methods, processing big data for ensemble clustering is sometimes slow or impossible. Usually, all primary clusters from all clustering methods are used in the consensus function. However, the merit of clusters from different methods can be considered to improve the consensus quality. Accordingly, we propose a cluster consensus selection approach that selects a subset of meriting primary clusters to participate in the final consensus. Here, Normalized Mutual Information (NMI) is developed to measure the merit of clusters. Meanwhile, reducing the number of primary clusters in the consensus function can enable big data clustering. The proposed algorithm is very computationally efficient and provides linear complexity in clustering. Experimental results show the effectiveness of the proposed algorithm in terms of different performance metrics such as NMI, ARI and CPCC.

An ensemble hierarchical clustering algorithm based on merits at cluster and partition levels

Ensemble clustering has emerged as a combination of several basic clustering algorithms to achieve high quality final clustering. However, this technique is challenging due to the complexities in primary clusters such as overlapping, vagueness, instability and uncertainty. Typically, ensemble clustering uses all the primary clusters into partitions for consensus, where the merits of a cluster or a partition can be considered to improve the quality of the consensus. In general, the robustness of a partition may be poorly measured, while having some high-quality clusters. Inspired by the evaluation of cluster and partition, this paper proposes an ensemble hierarchical clustering algorithm based on the cluster consensus selection approach. Here, the selection of a subset of primary clusters from partitions based on their merit level is emphasized. Merit level is defined using the development of Normalized Mutual Information measure. Clusters of basic clustering algorithms that satisfy the predefined threshold of this measure are selected to participate in the final consensus. In addition, the consensus of the selected primary clusters to create the final clusters is performed based on the clusters clustering technique. In this technique, the selected primary clusters are re-clustered to create hyper-clusters. Finally, the final clusters are formed by assigning instances to hyper-clusters with the highest similarity. Here, an innovative criterion based on merit and cluster size for defining similarity is presented. The performance of the proposed algorithm has been proven by extensive experiments on real-world datasets from the UCI repository compared to state-of-the-art algorithms such as CPDM, ENMI, IDEA, CFTLC and SSCEN.

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

AbstractEnsemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real‐world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC‐DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared.

Analysis of Power Load Characteristics Based on Adaptive Ensemble Clustering Algorithm

With the growing number of power users and more flexible power consumption, how to cluster the power load curves efficiently becomes particularly important. In order to improve the accuracy, effectiveness and rapidity of load clustering, an adaptive integrated fuzzy clustering algorithm was proposed in this paper. Firstly, the initial clustering centroids were obtained by the improved DPC algorithm; then, under the dual guarantee of Euclidean distance and membership, FCM algorithm was used to cluster load curves; finally, the optimal number of clusters was obtained by the improved adaptive method in the iterative process; thus, the autonomous and accurate clustering of power load curve was realized. The proposed algorithm, DPC algorithm and FCM algorithm were applied to the load clustering experiment of power system respectively. The comparative analysis shows that this algorithm is effective and superior for load curve clustering.

Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and L2,1-Norm.

Accurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locally weighted ensemble clustering (ELWEC) are two typical methods for ensemble clustering. However, DREC treats each microcluster equally and hence, ignores the differences between each microcluster, while ELWEC conducts clustering on clusters rather than microclusters and ignores the sample–cluster relationship. To address these issues, a divergence-based locally weighted ensemble clustering with dictionary learning (DLWECDL) is proposed in this paper. Specifically, the DLWECDL consists of four phases. First, the clusters from the base clustering are used to generate microclusters. Second, a Kullback–Leibler divergence-based ensemble-driven cluster index is used to measure the weight of each microcluster. With these weights, an ensemble clustering algorithm with dictionary learning and the -norm is employed in the third phase. Meanwhile, the objective function is resolved by optimizing four subproblems and a similarity matrix is learned. Finally, a normalized cut (Ncut) is used to partition the similarity matrix and the ensemble clustering results are obtained. In this study, the proposed DLWECDL was validated on 20 widely used datasets and compared to some other state-of-the-art ensemble clustering methods. The experimental results demonstrated that the proposed DLWECDL is a very promising method for ensemble clustering.

A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shaped

As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.

Clustering ensemble based on approximate accuracy of the equivalence granularity

Clustering ensemble deals with the inconsistent parts of base clusterings while maintaining consistency in the clustering results. The cluster core samples generally have more consistent neighbor relationship in base clusterings than the cluster edge samples, and these two types of samples have different contributions to the determination of the underlying data structure. In addition, in clustering ensemble, some samples that are equivalent in base clusterings can be merged into equivalence granularity to reduce the size of input data. Based on these ideas and combined with rough set theory, we propose a novel clustering ensemble algorithm based on the approximate accuracy of equivalence granularity. We initially define the clustering equivalence relation, extract the consistent results of base clusterings based on this relation, and upgrade the clustering ensemble from the sample level to the equivalence granularity level. Afterward, we use the approximate accuracy of the rough set to quantify the contribution of equivalence granularity in discovering the underlying data structure, divide the equivalence granularities into core and edge equivalence granularities, and implement different clustering processing strategies. The visual experiments on four synthetic data sets and comparison experiments with 14 state-of-the-art clustering ensemble algorithms on 21 data sets respectively prove the rationality and excellent performance of our proposed algorithm.

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.

Ensemble learning using three-way density-sensitive spectral clustering

As one popular clustering algorithm in the last few years, spectral clustering is advantageous over most existing clustering algorithms. Although spectral clustering can perform well in many instances, the algorithm still has some problems. The clusters obtained by spectral clustering have crisp boundaries, which cannot reflect the fact that one cluster may not have a well-defined boundary in the real situations. Furthermore, the frequently-used distance measures in spectral clustering cannot satisfy both global and local consistency, especially for the data with multi-scale. In order to address the above limitations, we firstly present a three-way density-sensitive spectral clustering algorithm, which uses the core region and the fringe region to represent a cluster. In the proposed algorithm, we use density-sensitive distance to produce a similarity matrix, which can well capture the real data structures. An overlap clustering is introduced to obtain the upper bound (unions of the core regions and the fringe regions) of each cluster and perturbation analysis is applied to separate the core regions from the upper bounds. The fringe region of the specific cluster is the differences between the upper bound and the core region. Because a single clustering algorithm cannot always achieve a good clustering result, we develop an improved ensemble three-way spectral clustering algorithm based on ensemble strategy. The proposed ensemble algorithm randomly extracts feature subset of sample and uses the three-way density-sensitive clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, voting method is used to generate a three-way clustering result. The experimental results show that the three-way density-sensitive clustering algorithm can well explain the data structure and maintain a good clustering performance at the same time, and the ensemble three-way density-sensitive spectral clustering can improve the robustness and stability of clustering results.

Clustering Ensemble Based on Hybrid Multiview Clustering

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.

A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis

Data analysis is the foundation of Internet of Things (IoT) based applications, and clustering is an effective technology of data analysis. Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the clustering performance in stability and robustness. However, it is difficult for existing clustering ensemble algorithms to achieve a satisfying ensemble result, when the base clustering results are unreliable. Concerning this problem, we develop a new clustering ensemble model in this paper, which has several advantages compared with traditional algorithms: (i) structure information about the data is effectively extracted from the base clusterings; (ii) data characteristics and structure information are integrated in an elegant fashion, in the production of the consensus clustering result; and (iii) our model has the generative ability that makes the model achieve outstanding performance when training samples are insufficient. In our model, the structural information is extracted by explicating the coupling relationships between base clusterings and between samples in clustering members. Then, data characteristics and structure information are combined in a generative graph representation learning framework. And the objectives of representation learning and consensus clustering are integrated into a unified optimization model, in which the prior distribution of the data is approximated by a Gaussian mixture model (GMM). Extensive experiments are conducted on multiple IoT datasets; the results prove that our model not only performs better than the conventional clustering ensemble algorithms but also outperforms the state-of-the-art deep clustering methods.

Markov clustering ensemble

Clustering ensemble is an unsupervised ensemble learning method that is very important in machine learning, since it integrates multiple weak base clustering results to produce a strong consistency result. This paper proposes the Markov clustering ensemble (MCE) model to solve the weak stability and robustness of soft clustering ensemble. First, the base clustering algorithms are regarded as new features of the original datasets. Then, the results of the base clustering algorithms are the values of these features, which can break through the framework of consensus cluster ensemble. Second, as the base clustering results are discrete data, the maximum information coefficient is applied to measure their similarity. Accordingly, a graph-based cluster ensemble model can be constructed with row vectors as vertices and the similarity between row vectors as edges. Then, the Markov process can be applied to infer the graph-based cluster ensemble model; therefore, the MCE algorithm is designed according to the inference. To test the performance of the MCE algorithm, clustering algorithms and clustering ensemble algorithms are used to conduct comparative experiments on ten datasets. The experimental results show that the MCE algorithm outperforms the other algorithms in terms of accuracy and purity.

An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement

The advent of architectures such as the Internet of Things (IoT) has led to the dramatic growth of data and the production of big data. Managing this often-unlabeled data is a big challenge for the real world. Hierarchical Clustering (HC) is recognized as an efficient unsupervised approach to unlabeled data analysis. In data mining, HC is a mechanism for grouping data at different scales by creating a dendrogram. One of the most common HC methods is Agglomerative Hierarchical Clustering (AHC) in which clusters are created bottom-up. In addition, ensemble clustering approaches are used today in complex problems due to the weakness of individual clustering methods. Accordingly, we propose a clustering framework using AHC methods based on ensemble approaches, which includes the clusters clustering technique and a novel similarity measurement. The proposed algorithm is a Meta-Clustering Ensemble scheme based on Model Selection (MCEMS). MCEMS uses the bi-weighting policy to solve the model selection associated problem to improve ensemble clustering. Specifically, multiple AHC individual methods cluster the data from different aspects to form the primary clusters. According to the results of different methods, the similarity between the instances is calculated using a novel similarity measurement. The MCEMS scheme involves the creation of meta-clusters by re-clustering of primary clusters. After clusters clustering, the number of optimal clusters is determined by merging similar clusters and considering a threshold. Finally, the similarity of the instances to the meta-clusters is calculated and each instance is assigned to the meta-cluster with the highest similarity to form the final clusters. Simulations have been performed on some datasets from the UCI repository to evaluate MCEMS scheme compared to state-of-the-art algorithms. Extensive experiments clearly prove the superiority of MCEMS over HMM, DSPA and WHAC algorithms based on Wilcoxon test and Cophenetic correlation coefficient.

Comparing stormwater quality and watershed typologies across the United States: A machine learning approach

Watersheds continue to be urbanized across different regions of the United States, increasing the number of impaired waterbodies due to urban stormwater. Using machine learning techniques, this study examined how stormwater quality and watershed characteristics are related at a national scale and compared stormwater quality across watersheds in diverse climates. We analyzed a selection of data from the National Stormwater Quality Database (NSQD) comprising 1,881 stormwater samples taken from 182 watersheds in 26 metropolitan areas in the United States between 1992 and 2003. Using an ensemble clustering algorithm, the stormwater quality in these samples was classified into "stormwater signatures," defined as distinct combinations of 9 contaminants including metals (Pb, Zn, Cu), particulates (TSS, TDS), and nutrients (BOD, TP, TKN, NOx). Next, multinomial logistic regression was applied to the NSQD data now classified by signature and combined with climate, weather, land use, and imperviousness data obtained from multiple sources. The results yielded 5 stormwater signatures with distinct aquatic toxicity implications and relationships to climate, weather, land use, and imperviousness: Signature 1 ("Ecotoxic and Eutrophic"), defined by high median concentrations of contaminants, likely represents the first flush in moderate-to-high imperviousness watersheds; Signature 2 ("Reduced Nitrates") represents a wet season signature, particularly for dry climates; Signature 3 ("Potentially Eutrophic") represents the first flush in low imperviousness watersheds; Signature 4 ("Elevated Particulates and Metals") represents a wet season signature, particularly on warmer days; finally, Signature 5 ("Most Dilute") is primarily a regional signature associated with the warm, wet climate of the southeastern US. This study serves as a proof-of-concept demonstrating how machine learning techniques can be used to identify patterns in high-dimensional and highly variable data. Applied to stormwater quality, these techniques identify major patterns in stormwater quality across the United States using a stormwater signature approach, which examines how contaminants co-occur and under what climate, weather, land use, and impervious conditions. The findings point to dominant processes driving stormwater generation and inform watershed monitoring, green infrastructure planning, stormwater quality under climate change, and opportunities for public engagement.