Stable Clustering Research Articles

Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.

K-means-ten clustering algorithm based on min-max criterion and region division

Aiming at the problem of unstable clustering results and low solution accuracy of -means- + algorithm, an I--means-+ clustering algorithm based on min-max criterion and region division is proposed. Firstly, the min-max criterion is proposed to calculate the distance from each data point to the nearest center, and the data point with the largest distance is preferentially selected as the new cluster center to avoid the situation where multiple initial centers are clustered in the same cluster; secondly, the data points in the split cluster are divided into different regions, and a data point is selected in each region as the candidate center to increase the diversity of the candidate centers; finally, for the clusters that fail to pair, the new split cluster is reselected by gain to pair with the original deleted cluster again to improve the pairing success rate and further reduce the objective function value. Experimental results show that compared with the I means-+ algorithm, the proposed algorithm has an average improvement in solution accuracy −k −and a more stable clustering result kunder the premise of basically equivalent operation efficiency compared with 6.47%-means and kmeans+ algorithms, the proposed algorithm has higher solution accuracy.

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma

IntroductionAn increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomics-based features and molecular characteristics of lung nodules. MethodsConsensus clustering was applied to the radiomic features of 1212 patients to establish stable clustering. Clusters were illustrated using clinicopathological and next-generation sequencing. A classifier was constructed to further investigate the molecular characteristics in patients with paired computed tomography and RNA sequencing data. ResultsPatients were clustered into four clusters. Cluster 1 was associated with a low consolidation-to-tumor ratio, preinvasion, grade I disease, and good prognosis. Clusters 2 and 3 reported increasing malignancy with a higher consolidation-to-tumor ratio, higher pathologic grade, and poor prognosis. Cluster 2 possessed more spread through air spaces and cluster 3 reported a higher proportion of pleural invasion. Cluster 4 had similar clinicopathological features as cluster 1 except but a proportion of grade II disease. RNA sequencing indicated that cluster 1 represented nodules with indolent growth and good differentiation, whereas cluster 4 reported progression in cell development but still had low proliferative activity. Nodules with high proliferation were classified into clusters 2 and 3. In addition, the radiomics classifier distinguished cluster 2 as nodules harboring an activated immune environment, whereas cluster 3 represented nodules with a suppressive immune environment. Furthermore, signatures associated with the prognosis of early-stage LUAD were validated in external datasets. ConclusionsRadiomics features can manifest molecular events driving the progression of LUAD. Our study provides molecular insight into radiomics features and assists in the diagnosis and treatment of early-stage LUAD.

A COMPREHENSIVE ANALYSIS OF ENERGY EFFICIENT WIRELESS SENSOR NETWORK (WSN) PROTOCOLS

Wireless Sensor Networks (WSNs) include many tiny and low energy and cheaper nodes in different areas for measuring the environmental parameters like temperature and humidity. Hence, the usage of WSNs has become an adaptable means of data transfer across different fields due to the advancement of wireless technology. It is noted that these networks,which use nodes for event detection and identification, gather information by means of simple calculations and send it to a particular Base Station (BS) via gateway nodes. The applications of WSNs can be classified into monitoring and tracking,which are used in areas such as environmental monitoring, health diagnostics, military surveillance, and many others.nergy consumption is still a major issue of concern, sensor nodes in particular, use energy in sensing data and transmitting it. Energy savings techniques like clustering and routing protocols are important for increasing the life time of the network.hus, this research aims at analysing the different aspects of improving energy efficiency and network performance,especially in the context of the clustering algorithms, such as the Multi-level Stable and Energy Efficient Clustering (MSEEC) protocol. The paper discusses major issues and suggests some new combined routing approaches for WSNs with the problems connected with fault tolerance, topology, scalability, and power consumption.

Multi-objective Bayesian optimization with enhanced features for adaptively improved glioblastoma partitioning and survival prediction

Glioblastoma, an aggressive brain tumor prevalent in adults, exhibits heterogeneity in its microstructures and vascular patterns. The delineation of its subregions could facilitate the development of region-targeted therapies. However, current unsupervised learning techniques for this task face challenges in reliability due to fluctuations of clustering algorithms, particularly when processing data from diverse patient cohorts. Furthermore, stable clustering results do not guarantee clinical meaningfulness. To establish the clinical relevance of these subregions, we will perform survival predictions using radiomic features extracted from them. Following this, achieving a balance between outcome stability and clinical relevance presents a significant challenge, further exacerbated by the extensive time required for hyper-parameter tuning.In this study, we introduce a multi-objective Bayesian optimization (MOBO) framework, which leverages a Feature-enhanced Auto-Encoder (FAE) and customized losses to assess both the reproducibility of clustering algorithms and the clinical relevance of their outcomes. Specifically, we embed the entirety of these processes within the MOBO framework, modeling both using distinct Gaussian Processes (GPs). The proposed MOBO framework can automatically balance the trade-off between the two criteria by employing bespoke stability and clinical significance losses. Our approach efficiently optimizes all hyper-parameters, including the FAE architecture and clustering parameters, within a few steps. This not only accelerates the process but also consistently yields robust MRI subregion delineations and provides survival predictions with strong statistical validation.

Clustering of Gastrointestinal Microorganisms in Human Stool Samples from Ghana.

The study was conducted to identify cluster patterns of enteric microorganisms with potential etiological relevance for infectious gastroenteritis in stool samples of individuals from Ghana, which is a known high-endemicity setting for infectious gastroenteritis. These patterns were compared to previous observations with specimens from Colombian indigenous people in order to assess potentially stable clustering for temporally and spatially distinct populations from high-endemicity regions. By doing so, the study aimed to identify stable clusters as markers of microbial interaction with potential importance for etiological relevance assignment in cases of multiple enteric pathogen detections. Stool samples from 1569 Ghanaian individuals (875 from HIV patients, 30 from HIV-negative control adult patients, and 644 from children < 2 years of age) were assessed for enteric microorganisms by applying real-time PCR. As a result, nucleic acids of bacterial microorganisms were most frequently detected, followed by protozoa, microsporidia, and helminths. Interestingly, the cluster assessment confirmed interaction patterns known from the previous analysis with Colombian indigenous people, demonstrating a high likelihood of Blastocystis hominis for clustering with other microorganisms and a prominent, potentially mediating role of Dientamoeba fragilis for microbial interactions within the clusters. In conclusion, the assessment confirmed conserved clustering of enteric microorganisms with potential etiological relevance for human infectious gastroenteritis over geographically distinct high-endemicity settings. Furthermore, the composition of abundant microorganisms is more important than regional factors for the determination of the interplay of enteric microorganisms in the human gut. Thereby, some microbial pathogens and commensals seem more susceptible to a changing microbial composition in the human gut than others.

Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis

In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid-based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid-based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user.

Multi-kernel subspace stable clustering with exact rank constraints

The identification of cancer subtypes plays a critical role in the early diagnosis of cancer and the delivery of appropriate treatment. Clustering based on comprehensive multi-omic data often yields superior results than clustering using a single data type since each omic type may contain complementary information. However, existing clustering methods cannot fully explore the internal structural information within multi-omic data and can lead to unstable clustering results. This paper proposes a novel multi-omic clustering framework, Multi-Kernel Subspace Stable Clustering with Exact Rank Constraints (MKSSC-ERC), which can effectively explore the correlation and complementary information between different omics types. Specifically, we design a kernel selection criterion that considers both accuracy and diversity and each omic data gets a base kernel. Then, we extract a consistent affinity matrix for these base kernels using subspace segmentation with exact rank constraints, which can refine the extracted shared structures and make them robust to sample noises. In addition, we devise a strategy for selecting the optimal initial cluster centers, which significantly enhances the stability of clustering results. Simulation studies on benchmark multi-omic datasets illustrate substantial gains over existing state-of-the-art methods in survival analysis. MKSSC-ERC is available at https://github.com/xuzihan66/MKSSC-ERC.

LEED certification system for green buildings in China: Examining spatial differences, temporal evolution, and spatial overflow

This paper examines the spread of green building development in China by analyzing the implementation of the LEED certification system across 31 provinces from 2006 to 2022. To investigate spatial differences, spatial-temporal evolution, and spatial spillovers of green building development in China, we employed various statistical methods, including Standard Distance Ellipse, Dagum's Gini Coefficient, Kernel Density Estimation, Markov Chain Analysis, Moran's I Exponential Analysis, and Social Network Analysis. The findings reveal the formation of a green building cluster in a northeast-southwest orientation, at the core of the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions. There is compelling evidence of substantial regional disparities in green building development, primarily driven by inter-regional differences, which has resulted in a significant gap between the eastern and non-eastern regions. This study identifies the presence of “club convergence” and the “Matthew effect” in China's green building landscape, where spatial factors play a crucial role. Furthermore, a distinct and stable spatial clustering is observed, indicating a complex and multi-faceted spatial association network that goes beyond geographical proximity and neighborhood effects. This network reveals diverse positions and roles for provinces and cities. The eastern provinces and cities are found to exert strong control and dominance while also playing a significant siphoning role. On the other hand, the western provinces and cities have been marginalized, experiencing minimal dividend spillover from the development of other provinces.

Design of CSKAS-VANET model for stable clustering and authentication scheme using RBMA and signcryption

A public key infrastructure-enabled system authentication model is developed to provide essential security functions for Vehicular ad hoc networks (VANETs). An intelligent transportation system is provided by VANET, an emerging technology. Dedicated short-range communication is used to disseminate messages wirelessly. Communications may be hacked, and messages can be stolen or fabricated. Hence, authenticated communication is crucial in the VANET environment. Some parameters such as trust, authentication, privacy, and security are at high risk. This article suggests a VANET with secure authentication and trust-based clustering mechanisms to provide stable and secure communication. Initially, the Restricted Boltzmann Machine learning algorithm (RBMA) is used to select the cluster head, which depends upon trust, vehicle lifetime, and buffer level. Then, cluster members are formed, followed by grouping. Diffie–Hellman Hyperelliptic Curve Cryptography and cryptographic hash functions are used by signcryption for secure communication in VANET. Therefore, the essential component of the key agreement strategy that will give superior authentication is this signcryption mechanism. Over the medium access protocol layer, all of these security characteristics are updated. The proposed method of clustering signcryption key agreement scheme (CSKAS) approach reduces time complexity and increases packet delivery ratio which is vital in providing stable, secure communication.

Instance-aware diversity feature generation for unsupervised person re-identification

Unsupervised person re-identification (Re-ID) methods have made significant progress by exploiting contrastive learning from unlabeled data. However, the previous approaches including cluster-level or instance-level contrast loss, did not fully explore inherent commonality of each identified individual from unlabeled samples, where the divergence of individual cluster and convergence of different clusters leads to a set of noisy pseudo labels which may result in label noise accumulation. To address this issue, we propose an instance-aware diversity feature generation (IDFG) framework, which can form a stable clustering feature space by exhuming diverse counterparts of given exemplars to update memory dictionary of each cluster, so as to reduce the effect of noisy labels. Specifically, we combines instance segmentation and masked auto-encoder to generate foreground-invariant diversity counterparts of given exemplars to reduce inter-class convergence caused by background similarity between different identification instances. Further, we introduce an instance-aware diversity feature mining module, which gradually creates more reliable clusters to generate more robust pseudo labels by exploiting the compactness and independence of clustering to update the memory dictionary. Extensive experiments demonstrate that the proposed IDFG framework achieves impressive performances of 85.6%, 73.7%, and 31.0% mAP on Market1501, DukeMTMC-reID and MSMT17, respectively.

The Determination of Effective Modes of Wind-Bridge Coupling System Based on Stabilization Clustering Method

Modal identification is the core content of the full-bridge aeroelastic model wind tunnel test. Modal consistency is the most direct and effective basis to ensure that the model and the real bridge meet the similarity criteria. Accurate, simple and fast identification of modal parameters has very important practical significance for the evaluation and correction of aeroelastic models. In this paper, the eigensystem realization algorithm (ERA) and stochastic subspace identification (SSI) is improved, and the automatic identification theory of effective modes of bridge aeroelastic model based on stable clustering method is proposed. The stable diagram eliminates the unstable modes by comparing the results of different system orders, and the pedigree clustering further polarizes the stable modes to obtain multi-order and high-precision system modes. Numerical simulation examples and wind tunnel engineering examples show that the method has high accuracy and good adaptability, and can solve the problem of unstable damping identification results to a certain extent. In the wind tunnel test, the stable clustering method is one of the effective methods to identify the modal of the bridge aeroelastic model excited by the wind environment.

Longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance

Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.

Hybrid machine learning approach for trust evaluation to secure MANET from routing attacks

A new era in communication has been ushered in by MANET networks, in which users (nodes) interact with one another through a self-configuring network of handheld devices linked by wireless links. Nodes are capable of participating and enthusiastic about sending packets to other nodes. Consequently, the need for a routing protocol materializes. The most difficult aspect is dealing with the network’s dynamic topology as a result of node mobility. This is because limited resources like storage space, battery life, and bandwidth require a protocol that can quickly adapt to topology changes while periodically updating messages. On the other hand, security is another important aspect of routing since the involvement of attackers will exhaust the network resources. This paper addresses the main issue of designing a routing protocol that handles all the adversaries and achieves better efficiency. For that, we proposed a Hybrid Machine Learning (HyML) model which evaluates the. Initially, the network is segregated by the Secure Stable Clustering (SSC) approach which first verifies the node’s legacy and forms clusters based on stability. The HyML is designed by combining two important ML techniques such as ANN and fuzzy-C Means (FCM) algorithm. The ANN model learns multiple attributes of the trust value and computes the cumulative trust score. Next, FCM determines the node position upon trust score. After the computation of the trust value, optimal route selection is performed by the Spider Monkey Optimization (SMO) technique. The overall work is evaluated through comprehensive simulations based on network longevity, throughput, energy usage, PDR, attack detection efficiency, and delay.

Functional subtypes of synaptic dynamics in mouse and human

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study.

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

Key point calibrating and clustering for hard example mining of dense analogs in the fish-eye lens.

Recent years have witnessed widespread applications of the fish-eye lens with a wide field-of-view. However, its inherent distortion poses a big challenge to the intelligent recognition of dense analogs (IRDA) by convolutional neural networks (CNN). The major bottleneck of existing CNN models lies in their limited modeling capacity for distorted objects in fish-eye images, leading to the misclassification of hard examples. To further improve the accuracy of IRDA, we propose a novel key point calibrating and clustering (KPCC) algorithm based on the hemispherical projection model. Our method can effectively correct the hard example misclassification predicted by the CNN, significantly enhancing the performance of the IRDA. The experiments show that, as a light-weight computation calibrating and stable adaptive clustering method, the KPCC increases the precision and recall rate of IRDA on the intelligent retail dataset by 8.55% and 8.07%, respectively; compared with the classic Focalloss, QFocalloss, and OHEM (online hard example mining), it can mine hard examples more sufficiently, especially in the scene of distorted dense analog detection.

PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping

BackgroundClustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC).ResultsThe PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e−05), while using the Calinski–Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/.ConclusionsPWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.