Partitional Clustering Research Articles

Extended Quality (eQual): Radial threshold clustering based on n-ary similarity.

We are transforming Radial Threshold Clustering (RTC), an algorithm, into Extended Quality Clustering, an algorithm with several novel features. Daura et al's RTC algorithm is a partitioning clustering algorithm that groups similar frames together based on their similarity to the seed configuration. Two current issues with RTC is that it scales as making it inefficient at high frame counts, and the clustering results are dependent on the order of the input frames. To address the first issue, we have increased the speed of the seed selection by using -means++ to select the seeds of the available frames. To address the second issue and make the results invariant with respect to frame ordering, whenever there is a tie in the most populated cluster, the densest and most compact cluster is chosen using the extended similarity indices. The new algorithm is able to cluster in linear time and produce more compact and separate clusters.

Self-Supervised, Multi-View, Semantics-Aware Anchor Clustering

Data-driven artificial intelligence systems effectively enhance accuracy and robustness by utilizing multi-view learning to aggregate consistent and complementary information from multi-source data. As one of the most important branches of multi-view learning, multi-view anchor clustering greatly reduces the time complexity via learning similarity graphs between anchors and data instead of data-to-data similarities, which has gained widespread attention in data-driven artificial intelligence domains. However, two issues still exist in current methods: (1) They commonly utilize orthogonal regularization to enhance anchor diversity, which may lead to a distorted anchor distribution, e.g., some clusters might have few or even no corresponding anchors. (2) They only utilize view-sharing anchors to aggregate complementary and consistent information between views, which may fail to ensure anchor robustness due to the heterogeneity gap between views. To this end, self-supervised, multi-view, semantics-aware anchor clustering (SMA2C) is proposed, containing multi-view representation alignment, adaptive anchor selection, and global spectral optimization. Specifically, SMA2C devises dual-level contrastive learning on representations and clustering partitioning between views within a deep encoding–decoding architecture to achieve multi-granularity alignment of views with the heterogeneity gap. Meanwhile, SMA2C introduces adaptive anchor selection via filtering outliers and refining clusters to enhance correlations between clusters and anchors, which ensures the diversity and discriminability of anchors. Finally, extensive evaluations across four real-world datasets confirm that SMA2C establishes a new benchmark for multi-view anchor clustering. In particular, SMA2C achieves a 6.75% improvement in accuracy over the second-best result on the HW dataset.

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

This paper offers a comprehensive investigation into the forward and inverse kinematics of a wrist rehabilitation robot, utilizing the Denavit-Hartenberg method for forward kinematics (FK) and a geometric approach, as well as artificial neural networks (ANN) and adaptive Neuro-Fuzzy inference systems (ANFIS) for inverse kinematics (IK) analysis. While the geometric method entails precise parameter measurements and faces uncertainties, ANN and ANFIS are explored as potential remedies to enhance accuracy and robustness. Evaluating 11 different training functions sourced from existing literature, our study conducts a thorough assessment of their performance within an ANN network. We aim to pinpoint the most suitable training function for achieving optimal IK solutions in the context of a wrist rehabilitation robotic. Additionally, the ANFIS model, trained using Fuzzy C-Means (FCM), sets itself apart from Grid Partitioning (GP) and Subtractive Clustering (SC). Among the ANN training functions, Bayesian regularization with 5 hidden layers emerges as the most effective, yielding low root mean square error (RMSE) values of 0.003, 0.004, and 0.007 degrees for pronation/supination (P/S), abduction/adduction (AB/AD), and flexion/extension (F/E), respectively. Conversely, ANFIS, trained with FCM, demonstrates satisfactory yet less precise results, with RMSE values of 0.191, 0.082, and 0.165 degrees for P/S, AB/AD, and F/E, respectively. Despite its adequacy, ANFIS trails behind ANN, showcasing RMSE reductions of 98.4%, 95.1%, and 95.7% for P/S, AB/AD, and F/E angles, respectively. This study contributes to leveraging ANN and ANFIS for IK analysis in wrist rehabilitation robotics, highlighting the efficacy of ANN, particularly when employing Bayesian regularization, to enhance accuracy.

Large-scale multiobjective competitive swarm optimizer algorithm based on regional multidirectional search

Competitive swarm optimizer (CSO) based on multidirectional search plays a crucial role in addressing large-scale multiobjective optimization problems (LSMOPs). However, relying solely on uniform or cluster partitioning of the objective space for sampling, along with two search directions constructed with upper and lower boundaries of global variables, sometimes lacks consideration of regional information. This results in an inefficient search and hinders the global convergence of the algorithm. To solve these problems, this study proposes a large-scale multiobjective competitive swarm optimizer algorithm based on regional multidirectional search (AMSLMOEA). Firstly, an adaptive objective space partitioning method based on the evolutionary state of the population is designed to enhance the adaptability of partitioning. Secondly, an individual multidirectional search strategy is introduced. Considering the algorithm’s computational complexity, the strategy selects the optimal individual within each subregion and constructs four-directional search vectors based on the lower limit of the global decision variables and the upper limit of the individual decision variables within the subregion. To validate the effectiveness of AMSLMOEA, the performance is tested on four benchmark function sets. The results demonstrate that AMSLMOEA outperforms the vast majority of the compared algorithms in terms of the IGD and HV metrics.

Functional projection K-means

A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a functional K-means. The subspace and the partition are estimated simultaneously by minimizing the within deviance in the reduced space. This allows us to find new dimensions with a very low within deviance, which should correspond to a high level of discriminant power. However, in some cases, the total deviance explained by the new dimensions is so low as to make the subspace, and therefore the partition identified in it, insignificant. To overcome this drawback, we add to the loss a penalty equal to the negative total deviance in the reduced space. In this way, subspaces with a low deviance are avoided. We show how several existing methods are particular cases of our proposal simply by varying the weight of the penalty. The estimation is improved by adding a regularization term to the loss in order to take into account the functional nature of the data by smoothing the centroids. In contrast to existing literature, which largely considers the smoothing as a pre-processing step, in our proposal regularization is integrated with the identification of both subspace and cluster partition. An alternating least squares algorithm is introduced to compute model parameter estimates. The effectiveness of our proposal is demonstrated through its application to both real and simulated data. Supplementary materials for this article are available online.

Multi-Search Strategy-based Improved Water Flow Optimizer Algorithm for Cluster Analysis

Clustering is a popular technique that has proven its capability in diverse fields like data analytics, business intelligence, social mining, image recognition, document clustering and bioinformatics. This technique determines the valuable information from a pre-defined set of data and groups similar information into the same cluster. In literature, many algorithms have been presented based on several clustering approaches. It is observed that partitional clustering algorithms are widely popular due to there simplicity and easy implementation such as k-means, k-medoids, and k-harmonic means. However, traditional algorithms suffer from several limitations like being trapped in local optima and depending on the initial solution quality. Heuristic algorithms are also proposed to alleviate the problems of traditional clustering algorithms. But, sometimes, these algorithms also get stuck in local minima and exhibit a lack of balance between search mechanisms. Hence, this work presents an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. In the proposed IWFO algorithm, the initial solution is generated based on the logistic chaotic map instead of random initialization, and in turn, an optimal quality solution is generated. The search mechanism of the WFO algorithm is enhanced based on an improved search mechanism which is the combination of non-linear functions and the previous best solution. Further, the local optima issue is alleviated using a multi-start search mechanism. The efficacy of the proposed IWFO algorithm is evaluated using twelve benchmark clustering datasets and results are compared with seventeen clustering algorithms. The simulation results are assessed using intra-cluster distance (intra), standard deviation (SD), rank, accuracy rate (AR) and detection rate (DR) parameters. Further, a statistical test is also conducted to validate the efficacy of the proposed IWFO algorithm. The results showed that proposed IWFO algorithms obtain superior quality results on most of the datasets.

Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023)

ABSTRACTThe traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based partitioning, hierarchical, and density clustering algorithms over the past decade (2013–2023). A digital text‐mining‐based comprehensive search technique with multiple field‐specific keywords, inclusion measures, and exclusion criteria is employed to obtain the research landscape from the Scopus database. The Scopus‐obtained data is analyzed using the VOSViewer software tool and coded using the R statistical analysis tool. The analysis identifies the numbers of scholarly articles, diversities of article sources, their impact and growth patterns, details of most influential authors and co‐authors, most cited articles, most contributing affiliations and countries, and their collaborations, use of different keywords and their impact, and so forth. The study further explores the articles and reports the methodologies employed for designing MapReduce‐based counterparts of traditional partitioning, hierarchical, and density clustering algorithms and their optimizations and hybridizations. Finally, the study lists the main research challenges encountered in the past decade for MapReduce‐based partitioning, hierarchical, and density clustering. It suggests possible areas for future research to contribute further in this field.

Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

CSMDC: Exploring consistently context semantics for multi-view document clustering

Multi-view document clustering, which aims to discover clustering partitions based on multiple document views, has attracted increasing research interest. However, the potential advantages of incorporating context semantics to enhance multi-view document clustering are yet to be fully explored. To address the above limitation, we propose a deep multi-view document clustering model that explores consistent context semantics called CSMDC which consists of three modules. Specifically, a novel view-translator is designed to convert non-contextual document views into contextual views. With its help, all document views can be processed to obtain their semantic representations within the view-translator representation learning module. Then the data-based view consistency self-supervising module is developed to fine-tune the semantic representations of document views by jointly incorporating view-wise representation relevance and consistent clustering assignments. Additionally, the task-based document clustering module is employed to simultaneously improve the view semantic representations and document clustering results. To the best of our knowledge, this is the first study to explicitly apply consistent context semantics under the guidance of data-based and task-based objectives in multi-view document clustering. Comprehensive experimental results demonstrated the effectiveness of the proposed model.

A survey of genetic algorithms for clustering: Taxonomy and empirical analysis

Clustering, an unsupervised learning technique, aims to group patterns into clusters where similar patterns are grouped together, while dissimilar ones are placed in different clusters. This task can present itself as a complex optimization problem due to the extensive search space generated by all potential data partitions. Genetic Algorithms (GAs) have emerged as efficient tools for addressing this task. Consequently, significant advancements and numerous proposals have been developed in this field.This work offers a comprehensive and critical review of state-of-the-art mono-objective Genetic Algorithms (GAs) for partitional clustering. From a more theoretical standpoint, it examines 22 well-known proposals in detail, covering their encoding strategies, objective functions, genetic operators, local search methods, and parent selection strategies. Based on this information, a specific taxonomy is proposed. In addition, from a more practical standpoint, a detailed experimental study is conducted to discern the advantages and disadvantages of approaches. Specifically, 22 different cluster validation indices are considered to compare the performance of clustering techniques. This evaluation is performed across 94 datasets encompassing diverse configurations, including the number of classes, separation between classes, and pattern dimensionality. Results reveal interesting findings, such as the key role of local search in optimizing results and reducing search space. Additionally, representations based on centroids and labels demonstrate greater efficiency and crossover and mutation operators do not prove to be as relevant. Ultimately, while the results are satisfactory, real-world clustering problems introduce additional complexity, especially for algorithms aiming to determine the number of clusters, resulting in diminished performance and the need for new approaches to be explored. Code, datasets and instructions to run algorithms in the LEAL library are available in an associated repository, in order to facilitate future experiments in this environment.

Optimizing the energy values of solid biofuel through acidic pre-treatment: An evolutionary-based neuro-fuzzy modelling and feature importance analysis

The increasing focus on sustainable energy solutions and eco-friendly fuel alternatives has sparked interest in exploring methods to enhance the energy content of solid biofuels through pre-treatment processes. This study investigates the impact of acidic (H2SO4) pretreatment on the energy content of solid biofuel derived from watermelon peel waste using machine learning methods. The biofuel properties, and calorific value (CV) were determined experimentally for both H2SO4-treated and untreated biofuel samples. Subsequently, an Adaptive neuro-fuzzy inference system (ANFIS) model with Genetic Algorithm (GA) and Grid Partitioning (GP) clustering techniques was developed to predict heating value of the biofuel, with performance evaluation based on root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), Mean Absolute Error (MAE) and R2-values. The decision tree regressor was employed to evaluate the feature importance, predictive power and impact of each input variable on CV prediction based on Gini importance metrics. To establish its superior performance, the hybrid ANFIS-GA-GP model was compared with ordinary-ANFIS and ANN model using same metrics. Results show that acid-pretreatment increased the CV of the solid biofuel by 3.53 MJ/kg (31.83 % improvement) and reduced ash content by 1.77 %. FTIR analysis revealed surface modifications, and a shift in the C-O vibrational stretch, while SEM micrographs displayed denser surfaces and reduced porosity as indicated by a lesser fiber diameter in treated samples. The GP-clustered ANFIS-GA model with a triangular membership function (tri-MF) exhibited superior performance and higher accuracy (RMSE of 0.1309, MAPE of 9.343, MAD of 0.1036, MAE of 0.1110, and R2-value of 0.9773 at model training). Furthermore, a tree decision regressor identified Fixed carbon as the most significant predictor for CV with a Gini importance of 0.988375. This study demonstrates the substantial enhancement in the energy content of biofuel using acidic pre-treatment and insights into a cutting-edge approach of improving the combustion properties of solid biofuel with machine learning models. This significantly contributes to the field of sustainable bioenergy research.

Exploring meta-heuristics for partitional clustering: methods, metrics, datasets, and challenges

Partitional clustering is a type of clustering that can organize the data into non-overlapping groups or clusters. This technique has diverse applications across the different various domains like image processing, pattern recognition, data mining, rule-based systems, customer segmentation, image segmentation, and anomaly detection, etc. Hence, this survey aims to identify the key concepts and approaches in partitional clustering. Further, it also highlights its widespread applicability including major advantages and challenges. Partitional clustering faces challenges like selecting the optimal number of clusters, local optima, sensitivity to initial centroids, etc. Therefore, this survey describes the clustering problems as partitional clustering, dynamic clustering, automatic clustering, and fuzzy clustering. The objective of this survey is to identify the meta-heuristic algorithms for the aforementioned clustering. Further, the meta-heuristic algorithms are also categorised into simple meta-heuristic algorithms, improved meta-heuristic algorithms, and hybrid meta-heuristic algorithms. Hence, this work also focuses on the adoption of new meta-heuristic algorithms, improving existing methods and novel techniques that enhance clustering performance and robustness, making partitional clustering a critical tool for data analysis and machine learning. This survey also highlights the different objective functions and benchmark datasets adopted for measuring the effectiveness of clustering algorithms. Before the literature survey, several research questions are formulated to ensure the effectiveness and efficiency of the survey such as what are the various meta-heuristic techniques available for clustering problems? How to handle automatic data clustering? What are the main reasons for hybridizing clustering algorithms? The survey identifies shortcomings associated with existing algorithms and clustering problems and highlights the active area of research in the clustering field to overcome these limitations and improve performance.

Sectoral electricity micro-spatial load forecasting based on partitional clustering technique

<p>Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.</p>

Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization

Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization

Active Clustering Ensemble With Self-Paced Learning.

A clustering ensemble provides an elegant framework to learn a consensus result from multiple prespecified clustering partitions. Though conventional clustering ensemble methods achieve promising performance in various applications, we observe that they may usually be misled by some unreliable instances due to the absence of labels. To tackle this issue, we propose a novel active clustering ensemble method, which selects the uncertain or unreliable data for querying the annotations in the process of the ensemble. To fulfill this idea, we seamlessly integrate the active clustering ensemble method into a self-paced learning framework, leading to a novel self-paced active clustering ensemble (SPACE) method. The proposed SPACE can jointly select unreliable data to label via automatically evaluating their difficulty and applying easy data to ensemble the clusterings. In this way, these two tasks can be boosted by each other, with the aim to achieve better clustering performance. The experimental results on benchmark datasets demonstrate the significant effectiveness of our method. The codes of this article are released in https://Doctor-Nobody.github.io/codes/space.zip.

An autonomous centreless approach to chunk-wise data partitioning

AbstractIn this paper, a novel autonomous centreless algorithm is proposed for data partitioning. The proposed algorithm firstly constructs the nearest neighbour affinity graph and identifies the local peaks of data density to build micro-clusters. Unlike the vast majority of partitional clustering algorithms, the proposed algorithm does not rely on singleton prototypes, namely, centres or medoids of the micro-clusters to partition the data space. Instead, these micro-clusters are directly utilised to attract nearby data samples to form shape-free Voronoi tessellations, hence, being centreless and robust to noisy data. A fusion scheme is further implemented to fuse these data clouds with higher intra-cluster similarity together to attain a more compact partitioning of data. The proposed algorithm is able to perform data partitioning on a chunk-wise basis and is highly computationally efficient with the default distance measure. Therefore, it is suitable for both static data partitioning in offline scenarios and streaming data partitioning in online scenarios. Numerical examples on a variety of benchmark datasets demonstrate the efficacy of the proposed algorithm.

Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy

ABSTRACT Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy. Additionally, we examined the agreement of clustering partitions of the same runners at different speeds. Trunk and lower-body kinematics were captured from 84 trained runners at different speeds on a treadmill. We used Principal Component Analysis for dimensionality reduction and agglomerative hierarchical clustering to identify groups of runners with a similar technique, and we evaluated cluster agreement across speeds. Clustering runners at different speeds independently produced different partitions, suggesting single speed clustering can fail to capture the full speed profile of a runner. The two clusters identified using data from the whole range of speeds showed differences in pelvis tilt and duty factor. In agreement with self-optimisation theories, there were no differences in running economy, and no differences in participants’ characteristics between clusters. Considering inter-individual technique variability may enhance the efficacy of training designs as opposed to ‘one size fits all’ approaches.

Clustering with Minimum Spanning Trees: How Good Can It Be?

AbstractMinimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are relatively fast to compute. In this paper, we quantify the extent to which they are meaningful in low-dimensional partitional data clustering tasks. By identifying the upper bounds for the agreement between the best (oracle) algorithm and the expert labels from a large battery of benchmark data, we discover that MST methods can be very competitive. Next, we review, study, extend, and generalise a few existing, state-of-the-art MST-based partitioning schemes. This leads to some new noteworthy approaches. Overall, the Genie and the information-theoretic methods often outperform the non-MST algorithms such as K-means, Gaussian mixtures, spectral clustering, Birch, density-based, and classical hierarchical agglomerative procedures. Nevertheless, we identify that there is still some room for improvement, and thus the development of novel algorithms is encouraged.

Late Fusion Multiview Clustering via Min-Max Optimization.

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms. The code of this work is publicly released at: https://xinwangliu.github.io/Under-Review.

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-informed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSK-means procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSK-means algorithm – without modifying the input dataset – produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). The findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.