Clustering Ensemble Algorithm Research Articles

Toward data efficient anomaly detection in heterogeneous edge–cloud environments using clustered federated learning

Anomaly detection in edge–cloud scenarios stands as a critical means to ensure the security of network environment. Federated learning (FL)-based anomaly detection combines multiple data sources and ensures data privacy, making it a promising distributed detection method. However, FL-based anomaly detection system is usually affected by data heterogeneity and data bias, resulting in the inefficiency of data used for FL and the decline of detection performance. We propose an iterative federated clustering ensemble algorithm named IFCEA, in which we (1) establish a committee on the devices, and select the optimal participation for each device based on the evaluations of committee; (2) filter the clusters based on committee results, and exclude the biased clusters; (3) design an aggregation weight that reflects the degree of local distribution balance; (4) present a novel cluster initialization method, OneBiPartition, which adapts to the number of clusters and commences clustering federated task efficiently. IFCEA enhances the data quality used in FL-based anomaly detection system from two perspectives: device selection and participation weights, effectively addressing the issues of data heterogeneity and data bias faced during the FL training phase. Extensive experimental results on five network traffic datasets (the UNSW-NB15, CIC-IDS2017, CIC-IDS2018, CIC-DDoS2019 and BCCC-DDoS2024 datasets) demonstrate that our proposed framework outperforms in terms of detection metrics and convergence performance.

A clustering ensemble algorithm for handling deep embeddings using cluster confidence

Abstract Clustering ensemble, which aims to learn a robust consensus clustering from multiple weak base clusterings, has achieved promising performance on various applications. With the development of big data, the scale and complexity of data is constantly increasing. However, most existing clustering ensemble methods typically employ shallow clustering algorithms to generate base clusterings. When confronted with high-dimensional complex data, these shallow algorithms fail to fully utilize the intricate features present in the latent data space. As a result, the quality and diversity of the generated base clusterings are insufficient, thus affecting the subsequent ensemble performance. To address this issue, we propose a novel clustering ensemble algorithm for handling deep embeddings using cluster confidence (CEDECC) to improve the robustness and performance. Instead of simply combining deep clustering with clustering ensembles, we take into consideration that the performance of existing deep clustering methods heavily relies on the quality of low-dimensional embeddings generated during the pre-training stage. The quality of embeddings is unstable due to the influence of different initialization parameters. In CEDECC, specifically, we first construct a cluster confidence measure to evaluate the quality of low-dimensional embeddings. Typically, high-quality low-dimensional embeddings yield accurate clustering results with the same model parameters. Then, we utilize multiple high-quality embeddings to generate the base partitions. In the ensemble strategy phase, we consider the cluster-wise diversity and propose a novel ensemble cluster estimation to improve the overall consensus performance of the model. Extensive experiments on three benchmark datasets and four real-world biological datasets have demonstrated that the proposed CEDECC consistently outperforms the state-of-the-art clustering ensemble methods.

Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering analysis: A cross-sectional study

Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting. Our framework integrates supervised and unsupervised learning methodologies to remove data noise and address imbalanced datasets effectively. Through the utilization of imbalance ensemble methods and clustering algorithms such as K-means, we enhance predictive accuracy, particularly in non-clinical datasets where imbalances are prevalent. Our results demonstrate an improvement of 8 % in recall compared to existing literature, showcasing the efficacy of our approach. Moreover, our framework identifies clusters of patients at the highest risk using MADM techniques, providing insights into susceptibility to CHD. Fuzzy clustering techniques further assess the degree of risk for individuals within each cluster, enabling personalized risk evaluation. Importantly, our analysis reveals that unhealthy lifestyle factors, annual per capita income, nutrition, and folic acid supplementation emerge as crucial predictors of CHD occurrences. Additionally, environmental risk factors and maternal illnesses significantly contribute to the predictive model. These findings underscore the multifactorial nature of CHD development, emphasizing the importance of considering socioeconomic and lifestyle factors alongside medical variables in CHD risk assessment and prevention strategies. Our proposed framework offers a promising avenue for early identification and intervention, potentially mitigating the burden of CHD on affected individuals and healthcare systems globally.

Anchor-based fast spectral ensemble clustering

Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky problems. First, spectral clustering can identify clusters of arbitrary shapes, but the high time and space complexity limit its application in generating base clusterings. Most existing algorithms utilize k-means to generate base clusterings, and the clustering effect on nonlinearly separable datasets needs further improvement. Second, ensemble clustering algorithms should generate multiple base clusterings. Even if low-complexity algorithms are applied, the running time is also long, which seriously affects the application of ensemble clustering algorithms on large-scale datasets. To tackle these problems, we propose a fast K-nearest neighbors approximation method, construct an anchor graph to approximate the similarity matrix, and use singular value decomposition (SVD) instead of eigenvalue decomposition (EVD) to reduce the time and space complexity of conventional spectral clustering. At the same time, we obtain multiple base clusterings by running spectral embedding once. Finally, we convert these base clusterings into a bipartite graph and use transfer cut to get the final clustering results. The proposed algorithms significantly reduce the running time of ensemble clustering. Experimental results on large-scale datasets fully prove the efficiency and superiority of our proposed algorithm.

A multi-view ensemble clustering approach using joint entropy

Multi-view clustering can capture complementary and consistent information from different views, which is a core research topic in the fields of machine learning and pattern recognition. However, existing multi-view clustering methods ignore the quality of each base clustering cluster, which may affect the clustering performance. In this study, a multi-view ensemble clustering approach is proposed using joint entropy to evaluate the base clustering clusters. Firstly, an uncertainty index of base clustering clusters is defined using joint entropy which characterizes the importance and quality of each cluster. Secondly, a weighted co-association matrix is constructed using the uncertainty index, and useless entries are removed from the matrix, making the co-association matrix more reasonable. Thirdly, a candidate clusters selection strategy based on the stability index and a Multi-view Ensemble Clustering (MvEC-DoS) algorithm is proposed. In the end, experiments on five benchmark datasets validate the efficacy of our approach compared to other state-of-the-art methods.

A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

A two-stage clustering ensemble algorithm applicable to risk assessment of railway signaling faults

Knowledge graph (KG) modeling constructs a connected network of hazard/fault events by drawing on historical reports, helping extract risk factors and propagation features in railway signaling safety. For the huge graph structure of the knowledge system, efficiently and accurately transforming the event information with text for risk level prediction is the crucial topic of this paper. To establish a quantitative risk assessment method based on KG modeling, we innovatively introduce text clustering technology to intelligently divide the entity short text data set in the KG, assign standardized entity names to the cluster partitions, and then complete the calculation and analysis according to the characteristic parameter formula, greatly reducing the labor and time consumption of data annotation and approximate text repetitive processing. Significantly, a two-stage clustering ensemble algorithm (TSCEA) integrating base clusterer optimization and hierarchical clustering prediction is proposed, whose innovative approach lies in describing sample similarity by the co-association matrix, while considering the contribution discrepancy of samples for decision-making. Specifically, Stage One selects and optimizes various base clusterers with different method kernels, while Stage Two emphasizes the contribution discrepancy of the cluster core and the cluster halo to make the final clustering decision, with the ensemble strategy serving as the bridge between these two. The KG established on field investigation is used to complete the experiment and analysis of the novel algorithm. Another clustering ensemble algorithm (based on a direct weighting strategy) and each base clusterer are applied to verify clustering performance, while the evaluation depends on external indicators. The results indicate that the proposed clustering ensemble algorithm outperforms others in various indicators, significantly improving clustering accuracy and robustness.

Improved Selective Deep-Learning-Based Clustering Ensemble

Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.

A novel member enhancement‐based clustering ensemble algorithm

SummaryClustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement‐based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co‐association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state‐of‐the‐art ensemble clustering algorithms, that is, cluster‐based similarity partitioning algorithm (CSPA), meta‐clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA, MCLA, HBGF, EAC, LWEA, and LWGA algorithms. The experimental results demonstrate that MECE algorithm is a valid approach to deal with the clustering ensemble problems.

A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications.

In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters. The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.

Towards improving community detection in complex networks using influential nodes

Abstract Complex network analysis is inspired by empirical studies of real-world networks such as computer networks, technology networks and social networks. The analysis of community structure in complex networks is understood as an important issue by the research society. A community is a set of nodes in a network where the density of connections is high. The insight in the literature shows many approaches to identify influential nodes, but these approaches only lead to finding community centres. Meanwhile, clustering techniques are effectively used for community detection, where they can reveal group structure and hidden connections by considering topological and demographic information. This article presents an ensemble clustering algorithm based on influential nodes to improve community detection in complex networks. Considering different characteristics of the network, the proposed method seeks to discover common interests between users and their behaviours to identify the most suitable communities. First, a set of influential nodes are identified as community centres. Then, these centres are considered as cluster centres. After that, primary clusters are created based on the determined centres. Finally, the primary clusters are reclustered to form the final clusters. Here, the final clusters are considered as communities of users in the network. The simulation has been performed on real-world networks and the results confirm the effectiveness of the proposed method. Specifically, the communities identified by the proposed method are 2.1% better than the best existing state-of-the-art method in terms of modularity. Keywords: complex network; community detection; influential nodes; ensemble clustering.

Dual-level clustering ensemble algorithm with three consensus strategies

Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years and has achieved numerous noteworthy breakthroughs. Nevertheless, three key issues persist: (1) the majority of CE selection strategies rely on preset parameters or empirical knowledge as a premise, lacking adaptive selectivity; (2) the construction of co-association matrix is excessively one-sided; (3) the CE method lacks a more macro perspective to reconcile the conflicts among different consensus results. To address these aforementioned problems, a dual-level clustering ensemble algorithm with three consensus strategies is proposed. Firstly, a backward clustering ensemble selection framework is devised, and its built-in selection strategy can adaptively eliminate redundant members. Then, at the base clustering consensus level, taking into account the interplay between actual spatial location information and the co-occurrence frequency, two modified relation matrices are reconstructed, resulting in the development of two consensus methods with different modes. Additionally, at the CE consensus level with a broader perspective, an adjustable Dempster–Shafer evidence theory is developed as the third consensus method in present algorithm to dynamically fuse multiple ensemble results. Experimental results demonstrate that compared to seven other state-of-the-art and typical CE algorithms, the proposed algorithm exhibits exceptional consensus ability and robustness.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness, trajectory prediction, threat assessment and maneuver decision. To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data, and automatically and adaptively complete the task of extracting the target maneuver pattern, in this paper, an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder, G-G clustering algorithm and the selective ensemble clustering analysis algorithm. Firstly, the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension; Then, taking the time information into account, the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm, and a large number of maneuver primitives are extracted; Finally, the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm, which can prove that each class represents a maneuver action. The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3% of maneuver actions，indicating that the method is effective and satisfies the requirements for engineering accuracy. In addition, this method can provide data support for various target maneuvering recognition methods proposed in the literature, greatly reduce the workload and improve the recognition accuracy.

Weighted ensemble clustering with multivariate randomness and random walk strategy

Ensemble clustering algorithms have made significant progress in recent years due to their excellent performance. However, most of these algorithms face two challenges: one is to focus on the selection of subspaces since there is limited discussion on how to construct a potential metric space, the other is to treat basic clustering equally without fully considering the local connection between clusters when constructing the cooperative association matrix. To solve these issues, we propose a weighted ensemble clustering algorithm with multiple randomness and random walk strategy. We define the free exponential similarity kernel to create a diverse set of random metric spaces coupled with random subspaces and use spectral clustering to generate base clustering. Moreover, we use random walk strategy to discover the local connection between clusters and weight the collaborative association matrix. Finally, the collaborative association matrix uses consensus functions based on hierarchical clustering and meta clustering to obtain clustering results. On this basis, two specific ensemble clustering algorithms WECMR-HC and WECMR-MC are proposed. Theoretical analysis and experimental results demonstrate that our proposed algorithm outperforms existing ensemble algorithms.

An improved weighted ensemble clustering based on two-tier uncertainty measurement

Existing locally weighted ensemble clustering algorithms strive to weight each cluster and take into account the differences among all clusters, but they tend to ignore the basic cluster labels. The purpose of this paper is to combine the influence of cluster level and the base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts a global weighting strategy into a local ensemble cluster learning framework. First, the cluster uncertainty based on an entropy criterion is refined by considering the base clustering labels for each cluster. Then, the two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. Experiments validate the effectiveness of the proposed TWEC framework by comparing it with ten comparison algorithms on fourteen real-world datasets and twenty synthetic datasets. The results show that TWEC framework can improve the robustness and stability of clustering.

Detection of types cyber-bullying using fuzzy c-means clustering and xgboost ensemble algorithm

In this study a neural network model (XGB_CTD) that will prediction which type of bullying the users may expose to, through dataset gained by the cyberbullying scale applied to the young internet users is formulated. Extreme Gradient Boosting (XGboost) algorithm, one of the ensemble learning methods is used in this method. There while this model contains 13 input parameters taken from the scale, there exist one output parameter classified one of the 9 outputs. The reliability of the data set obtained through survey is confirmed by statistical methods. Data set has been fragmented with Fuzzy C-Means (FCM) which is one of fuzzy clustering algorithms. Hyper-parameters for the maximum efficiency of the model training have been defined as model, learning and boosting method. Independent variables in data set have been scaled through standard normalization. As a result, the model has yielded % 91,75 accuracy rate in prediction of the classification as 9 different cyberbullying types. The same data set has been trained by different machine learning algorithms. It is seen that the proposed model has reached the highest accuracy when compared to the conventional machine learning algorithms. This study aims at prediction cyberbullying through the proposed model including different questions without claim by the young users as they were bullied. Similarly, type of the cyberbullying will also be able to be estimated by the help of internet using habits of the young users. Therefore, it is thought that the young can be prevented from experiencing psychological pressure or digital life fear.

Reading Multilevel 2-D Barcodes Using a Machine Learning Approach

This paper addresses the reading problem of multi-level 2D barcodes over a Print-and-Capture (PC) channel. The prior reading schemes have different limitations to hinder their applications, e.g., suffering from quantization error, being sensitive to the predetermined decision boundaries, and being sensitive to the selection of initial parameters. In this paper, we introduce a machine learning approach to address the above limitations using a new ensemble clustering algorithm. Based on the new ensemble clustering algorithm, we propose two reading schemes of a multi-level 2D barcode. Specifically, the first proposed scheme is named the Ensemble Clustering (EC) reading scheme. In the EC reading scheme, we introduce a weighted ensemble mechanism to assign different weights to different base clustering results. Then, we propose the second scheme, named the Enhanced Ensemble Clustering (EEC) reading scheme, to further improve the reading performance with the help of the reference symbols. We implement our approach and conduct extensive performance comparisons through an actual excremental platform under various multi-level 2D barcodes and various capturing devices. From experimental results, we observe that both proposed reading schemes have better performance than the prior reading schemes. Moreover, the EEC reading scheme has better performance than the EC reading scheme, and their performance gap becomes more apparent as the distortion of a PC channel increases.

Fuzzy-Rough induced spectral ensemble clustering

Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzy-rough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm.

A Study on Enhanced Spatial Clustering Using Ensemble Dbscan and Umap to Map Fire Zone in Greater Jakarta, Indonesia

This research investigated ensemble clustering algorithms and dimensionality reduction for fire zone mapping, specifically DBSCAN + UMAP. We evaluated six clustering methods: DBSCAN, ensemble DBSCAN, DBSCAN + UMAP, ensemble DBSCAN + UMAP, HDBSCAN and Gaussian Mixture Model (GMM). We evaluated our results based on the Silhouette Score and the Davies-Bouldin (DB) index, emphasizing handling irregular cluster shapes, smaller clusters and resolving incompact clusters. Our findings suggested that ensemble DBSCAN + UMAP outperformed five other methods with zero noise clusters indicating clustering results are resistant to outliers, leading to a clearer identification of fire-prone areas, a high Silhouette Score of 0.971, indicating accurate cluster separation of distinct areas of potential fire hazards and an exceptionally low DB Index of 0.05 that indicates compact clusters to identify well-defined and geographically concentrated areas prone to fire hazards. Our findings contribute to the advanced techniques for minimizing the impacts of fires and improving fire hazard assessments in Indonesia.