Partitional Clustering Approach Research Articles

Graph convolutional network for water network partitioning

The partitioning of a water distribution network into several district-metered areas (DMAs) yields is of great benefits for the management of water distribution systems, including enhanced leak control, pollution monitoring, and pressure optimization. However, achieving an effective partitioning that simultaneously incorporates node characteristics and network connections is challenging. Previous studies have tackled water network partitioning by focusing on either node features or pipe connections individually. In response to the limitations of current approaches for water distribution network partitioning, this study introduces an innovative unsupervised clustering approach for node partitioning based on graph convolutional network (GCN) techniques. Initially, the network nodes are clustered using the Cross-Attention Fusion Based Enhanced Graph Convolutional Network (CaEGCN) deep clustering algorithm, considering both the network’s topological layout and hydraulic properties. The resulting clusters categorize the network nodes into distinct regions. We evaluate our approach and other methods through a real world water distribution network using five different metrics commonly used to evaluate applied in assessing water supply network zoning issues. By comparing and analyzing the performance of experiments with other schemes, the experimental results demonstrate that the CaEGCN deep clustering method featured a remarkable performance. The proposed method showcases its potential for practical application by offering a dependable and easily interpretable water network partitioning solution.

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>

Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning

Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning

An advanced ensemble clustering approach for data partitioning and mining to optimize performance in variable refrigerant flow systems

This study presents a data mining-based approach to develop clustering ensemble models for data partitioning of variable refrigerant flow (VRF) system, with the aim of improving the quality of pattern detection and laying a solid foundation for further data mining. This approach mainly includes three parts. First, data preprocessing combines handling outliers, feature filtering, stable data filtering and data normalization. Secondly, consensus clustering is used to obtain an appropriate number of clusters. To generate base clusterings, we applied three clustering algorithms - k-means, self-organizing maps, and fuzzy C-means - in a 1:1:1 ratio. These base clusterings were selected based on the adjusted rand index as the underlying selection criterion. For clustering ensemble, we utilized three integration methods, namely K-modes, latent class analysis, and linkage clustering ensemble. Finally, analyze the metric difference and time consumption of the integration clustering results of different methods, and obtain a suitable data partitioning scheme for VRF system. This case study examines the utilization of the proposed method to analyze the operation data of a VRF system in an office building. The results indicate that the quality of the clustering derived by the ensemble clustering model is notably superior to that of the individual base clustering. Moreover, the latent class analysis method demonstrates remarkable advantages in achieving top-quality clustering outcomes. These findings suggest that the proposed method can be effective in optimizing VRF system operation while leveling up the accuracy of data analysis. Ensemble models have considerable merit in devising fault identification and diagnosis techniques, optimizing operation, and exploring the links between VRF systems and smart grids.

A Partitional Clustering Approach for the Identification and Analysis of Coexisting Bacteria in Groups of Bacterial Vaginosis Patients

A Partitional Clustering Approach for the Identification and Analysis of Coexisting Bacteria in Groups of Bacterial Vaginosis Patients

A survey on the utilization of Superpixel image for clustering based image segmentation.

Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.

Combining Statistical Clustering with Hydraulic Modeling for Resilient Reduction of Water Losses in Water Distribution Networks: Large Scale Application Study in the City of Patras in Western Greece

Partitioning of water distribution networks (WDNs) into pressure management areas (PMAs) or district metered areas (DMAs) is the most widely applied method for the efficient management and reduction of real losses (leakages). Although PMA partitioning is a crucial task, most clustering methods are strongly affected by user-defined weighting factors that heavily affect the final outcome while being associated with heavy computational loads, leading to time-consuming applications. In this work, we use hierarchical clustering enriched with topological proximity constraints to develop an approach for the optimal sizing and allocation of PMAs (or DMAs) in water distribution networks that seeks to minimize water leakages while maintaining a sufficient level of hydraulic resilience. To quantify the latter, we introduce a resilience index that accounts for water leakages and nodal heads in pressure-driven and mixed pressure-demand ways, respectively. The strong points of the introduced approach are that (1) it uses the original pipeline grid as a connectivity matrix in order to avoid unrealistic clustering outcomes; (2) it is statistically rigorous and user unbiased as it is based solely on statistical metrics, thus not relying on and/or being affected by user-defined weighting factors; and (3) it is easy and fast to implement, requiring minimal processing power. The effectiveness of the developed methodology is tested in a large-scale application study in four PMAs (namely Boud, Kentro, Panahaiki, and Prosfygika) of the city of Patras in western Greece, which cover the entire city center and the most important part of the urban fabric of Patras, consisting of approximately 202 km of pipeline and serving approximately 58,000 consumers. Due to its simplicity, minimal computational requirements, and objective selection criteria, the suggested clustering approach for WDN partitioning can serve as an important step toward developing useful decision-making frameworks for water experts and officials, allowing for improved management and reduction of real water losses.

NDPD: an improved initial centroid method of partitional clustering for big data mining

PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

High-Risk Obesity Phenotypes: Target for Multimorbidity Prevention at the ROFEMI Study.

Background: Describe the profile of patients with obesity in internal medicine to determine the role of adiposity and related inflammation on the metabolic risk profile and, identify various “high-risk obesity” phenotypes by means of a cluster analysis. This study aimed to identify different profiles of patients with high-risk obesity based on a cluster analysis. Methods: Cross-sectional, multicenter project that included outpatients attended to in internal medicine. A total of 536 patients were studied. The mean age was 62 years, 51% were women. Patients were recruited from internal medicine departments over two weeks in November and December 2021 and classified into four risk groups according to body mass index (BMI) and waist circumference (WC). High-risk obesity was defined as BMI > 35 Kg/m2 or BMI 30–34.9 Kg/m2 and a high WC (>102 cm for men and >88 cm for women). Hierarchical and partitioning clustering approaches were performed to identify profiles. Results: A total of 462 (86%) subjects were classified into the high-risk obesity group. After excluding 19 patients missing critical data, two profiles emerged: cluster 1 (n = 396) and cluster 2 (n = 47). Compared to cluster 1, cluster 2 had a worse profile, characterized by older age (77 ± 16 vs. 61 ± 21 years, p < 0.01), a Charlson Comorbidity Index > 3 (53% vs. 5%, p < 0.001), depression (36% vs. 19%, p = 0.008), severe disability (64% vs. 3%, p < 0.001), and a sarcopenia score ≥ 4 (79% vs. 16%, p < 0.01). In addition, cluster 2 had greater inflammation than cluster 1 (hsCRP: 5.8 ± 4.1 vs. 2.1 ± 4.5 mg/dL, p = 0.008). Conclusions: Two profiles of subjects with high-risk obesity were identified. Based on that, older subjects with obesity require measures that target sarcopenia, disability, psychological health, and significant comorbidities to prevent further health deterioration. Longitudinal studies should be performed to identify potential risk factors of subjects who progress from cluster 1 to cluster 2.

A Novel Soft Clustering Approach for Gene Expression Data

Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to identify the patterns present in massive gene data. Many techniques involve hierarchical, partitioning, grid based, density based, model based and soft clustering approaches for dealing with the gene expression data. Understanding the gene regulation and other useful information from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate on providing a soft clustering approach for analyzing the gene expression data. The population elements are grouped based on the fuzziness principle and a degree of membership is assigned to all the elements. An improved Fuzzy clustering by Local Approximation of Memberships (FLAME) is proposed in this work which overcomes the limitations of the other approaches while dealing with the non-linear relationships and provide better segregation of biological functions.

Predicting H2S emission from gravity sewer using an adaptive neuro-fuzzy inference system

Abstract A predictive model to estimate hydrogen sulfide (H2S) emission from sewers would offer engineers and asset managers the ability to evaluate the possible odor/corrosion problems during the design and operation of sewers to avoid in-sewer complications. This study aimed to model and forecast H2S emission from a gravity sewer, as a function of temperature and hydraulic conditions, without requiring prior knowledge of H2S emission mechanism. Two different adaptive neuro-fuzzy inference system (ANFIS) models using grid partitioning (GP) and subtractive clustering (SC) approaches were developed, validated, and tested. The ANFIS-GP model was constructed with two Gaussian membership functions for each input. For the development of the ANFIS-SC model, the MATLAB default values for clustering parameters were selected. Results clearly indicated that both the best ANFIS-GP and ANFIS-SC models produced smaller error compared with the multiple regression models and demonstrated a superior predictive performance on forecasting H2S emission with an excellent R2 value of &amp;gt;0.99. However, the ANFIS-GP model possessed fewer rules and parameters than the ANFIS-SC model. These findings validate the ANFIS-GP model as a potent tool for predicting H2S emission from gravity sewers.

Estimation and Clustering in Popularity Adjusted Block Model

AbstractThe paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (Journal of the Royal Statistical Society Series B, 2018, 80, 365–386). We argue that the main appeal of the PABM is the flexibility of the spectral properties of the graph which makes the PABM an attractive choice for modelling networks that appear in biological sciences. We expand the theory of PABM to the case of an arbitrary number of communities which possibly grows with a number of nodes in the network and is not assumed to be known. We produce estimators of the probability matrix and of the community structure and, in addition, provide non-asymptotic upper bounds for the estimation and the clustering errors. We use the Sparse Subspace Clustering (SSC) approach for partitioning the network into communities, the approach that, to the best of our knowledge, has not been used for the clustering network data. The theory is supplemented by a simulation study. In addition, we show advantages of the PABM for modelling a butterfly similarity network and a human brain functional network.

Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets

Clustering is the most widely used unsupervised machine learning technique, having extensive applications in statistical analysis. We have multiple clustering algorithms available in theory and many more implementations available in practice. A bunch of literatures can be found focusing on the quality of clustering algorithms using various internal and external evaluation techniques. The motivation behind this work is the scarcity of literatures dealing with performance of clustering algorithms in terms of turnaround time. This paper summarizes the experimental analysis conducted on the performance of multiple clustering algorithms based on cardinality and dimensionality. The analysis is performed in R, which is a free and open source programming language mainly used for statistical computing. This work evaluates nine key algorithms coming under partitioning, hierarchical, density-based and model-based clustering approaches using different social media data sets. We captured performance trends of these algorithms in terms of turnaround time by varying the cardinality and dimensionality parameters of the data sets. Based on our experiments, CLARA, CLARANS, and k-means algorithms demonstrate best performances with varying cardinality. It is also observed that changes in dimensionality do not impact hierarchical clustering approaches whereas there is a positive influence on the execution time for partitioning, density-based and model-based clustering approaches.

Hybrid Clustering of Shared Images on Social Networks for Digital Forensics

Clustering the images shared through social network (SN) platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The sensor pattern noise (SPN) caused by the camera sensor imperfections during the manufacturing process can be extracted from the images and used to fingerprint the smartphones. The process of content compression performed by the SNs causes loss of image details and weakens the SPN, making the clustering task even more challenging. In this paper, we present a hybrid algorithm capable of clustering the images captured and shared through SNs without prior knowledge about the types and number of the acquisition smartphones. The hybrid method exploits batch partitioning, image resizing, hierarchical and graph-based clustering approaches to cluster the images. Using Markov clustering, the hierarchical clustering is conducted in such a way that the representative clusters with a higher probability of belonging to the same camera are selected for merging, which accelerates the clustering. For merging the clusters, the adaptive threshold updated iteratively through the hybrid clustering is used, which results in more precise clusters even for images from the same model of smartphones. The results on the VISION dataset, including both native and shared images , prove the effectiveness and efficiency of the hybrid method in comparison with the state-of-the-art SPN-based image clustering algorithms.

Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. Such datasets do not have a direct representation in Euclidean space. As a consequence, dissimilarity measures such as the Gower distance are used when partitioning clustering approaches are used with such datasets. Homogeneity analysis (HA) can be used to determine a Euclidean representation of mixed datasets. Such a representation can be analysed by leveraging the large body of tools and techniques for data with a Euclidean representation. The utility of the representation obtained from HA is not limited to clustering. This representation can be used to visualize mixed datasets and generate succinct numerical summaries. Such summaries can yield clues about associations between variables which may be difficult to discover otherwise. AMS Classification Code: 62-07

On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows.

For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.

Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM–IDPSO and FCM2–IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.

Using Hierarchical Clustering and Dendrograms to Quantify the Clustering of Membrane Proteins

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance. Two points from a pattern were put in the same cluster if they were closer than this distance. In this study, we present a new methodology based on hierarchical clustering to quantify clustering. An intrinsic distance is computed, which is the distance that produces the maximum number of clusters in the biological data, eliminating the need to choose a distance. To quantify the extent of clustering, we compare the clustering distance between the experimental data being analyzed with that from simulated random data. Results are then expressed as a dimensionless number, the clustering ratio that facilitates the comparison of clustering between experiments. Replacing the chosen cluster distance by the intrinsic clustering distance emphasizes densely packed clusters that are likely more important to downstream signaling events.We test our new clustering analysis approach against electron microscopy images from an experiment in which mast cells were exposed for 1 or 2 minutes to increasing concentrations of antigen that crosslink IgE bound to its high affinity receptor, FcϵRI, then fixed and the FcϵRI β subunit labeled with 5nm gold particles. The clustering ratio analysis confirms the increase in clustering with increasing antigen dose predicted from visual analysis and from the Hopkins statistic. Access to a robust and sensitive tool to both observe and quantify clustering is a key step toward understanding the detailed fine scale structure of the membrane, and ultimately to determining the role of spatial organization in the regulation of transmembrane signaling.

Efficient clustering and simulated annealing approach for circuit partitioning

Circuit net list bipartitioning using simulated annealing technique has been proposed in the paper. The method converges asymptotically and probabilistically to global optimization. The circuit net list is partitioned into two partitions such that the number of interconnections between the partitions is minimized. The proposed method begins with an innovative clustering technique to obtain a good initial solution. Results obtained show the versatility of the proposed method in solving non polynomial hard problems of circuit net list partitioning and show an improvement over those available in literature.

An improved branch-and-bound clustering approach for data partitioning

In this paper, we are concerned with clustering algorithms for vertical partitioning. In particular, we examine the use of a branch-and-bound scheme. An existing algorithm using such a scheme may produce infeasible solutions to some problems. We adopt the same branch-and-bound scheme and develop a new branching strategy to avoid infeasibility. Illustrative examples are used to demonstrate the effectiveness of our new approach. In addition, we also show how to formulate the horizontal partitioning problem such that the same algorithm can be applied.