Data Clustering Method Research Articles

Heterogeneous density-based clustering with a dual-functional memristive array.

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Graph-Based Multicentroid Nonnegative Matrix Factorization.

Nonnegative matrix factorization (NMF) is a widely recognized approach for data representation. When it comes to clustering, NMF fails to handle data points located in complex geometries, as each sample cluster is represented by a centroid. In this article, a novel multicentroid-based clustering method called graph-based multicentroid NMF (MCNMF) is proposed. Because the method constructs the neighborhood connection graph between data points and centroids, each data point is represented by adjacent centroids, which preserves the local geometric structure. Second, because the method constructs an undirected connected graph with centroids as nodes, in which the centroids are divided into different centroid clusters, a novel data clustering method based on MCNMF is proposed. In addition, the membership index matrix is reconstructed based on the obtained centroid clusters, which solves the problem of membership identification of the final sample. Extensive experiments conducted on synthetic datasets and real benchmark datasets illustrate the effectiveness of the proposed MCNMF method. Compared with single-centroid-based methods, the MCNMF can obtain the best experimental results.

A Streaming Data Clustering Method Based on Dual Strategies Improved DENCLUE

A Streaming Data Clustering Method Based on Dual Strategies Improved DENCLUE

An Exploration of Chinese-Russian Youth Humanistic Exchanges and Cooperation Based on VAR Modeling

Abstract In the cross-cultural context, interpersonal trust and cooperative behaviors become more complex due to mutual adaptation, communication and interaction between different cultures and races. This study explores the self-perception and materialism of Chinese and Russian youth in humanistic exchanges and the impact on interpersonal trust and cooperation behavior. This paper constructs a humanistic exchange and cooperation evaluation model based on VAR. We apply the panel data clustering method based on structural relationships to model and analyze heterogeneous and cross-sectionally independent PVAR. The variable coefficient PVAR is divided into K classes according to the structural relationship aggregation to reduce the number of parameters estimated by the model. The model estimation method of PVAR based on relational clustering in the case of cross-section independence is proposed. Among the various scores of humanistic exchanges between Chinese and Russian youths, the academic self dimension score of self-perception is positively proportional to the materialistic desire dimension score of materialism, with a correlation coefficient of 0.376. From the test of the differences in interpersonal trust and cooperative behavior between Chinese and Russian youths, Chinese youths’ interpersonal trust (M=5.36) and collective behavior (M=36.18) are higher than those of Russian youths’ interpersonal trust (M=4.27) and cooperative behavior (M=33.94). The combined results show that there is still a need to improve the cross-cultural adaptation of Russian youth coming to China and to enhance cooperative behavior with others.

Methodology of the countries’ economic development data analysis

The paper examines the issue of improving the methods of identification of economic objects and their analysis using algorithms of intelligent data processing. The use of the developed methodology in the economic analysis allows for improvement in the quality of management. It can be the basis for creating decision support systems to prevent potentially dangerous changes in the economic status of the research object. In this work, an improved method of c-means data clustering with agent-oriented modification is proposed, and a radial-basis neural network and its extension are proposed to determine whether the obtained clusters are relevant and to analyze the informativeness of state variables and obtain a subset of informative variables. The effect of applying data compression using an autoencoder on the accuracy of the methods is also considered. According to the results of testing of the developed methodology, it was proved that the probability of incorrect determination of the state was reduced when identifying the states of economic systems, and a reduced value of the error of the third kind was obtained when classifying the states of objects.

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.

SVM-based fast 3D pore-scale rock-typing and permeability upscaling for complex rocks using Minkowski functionals

The rapid advancement of digital core analysis has greatly promoted the research progress of flow and transport in porous media. However, complex analytical process with exceeding computational load impedes the application on large data volume. Considering the strong heterogeneity of the underground porous media, the integration of pore-scale information into continuum scale is widely concerned for the future development of digital physical analysis. For hierarchical porous structures, pore-scale rock-typing and upscaling of petrophysical properties is a promising solution towards the issue, and morphological and topological descriptors associating data clustering methods are popularly utilized. However, the size of the regional support through which the parameter fields are generated heavily affects the descriptive capacities of the parameters and the following partitioning process. We propose in this work a robust integrated pore-scale rock-typing and upscaling technology for 3D porous structures which uses Minkowski functionals as the descriptive parameters. A fast-computational method utilising fast Fourier transform has been applied for efficient generation of the parameter fields. A comparative study between the two different classification methods of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) has been conducted on two complex artificial porous systems and a laminated sandstone through various regional support sizes. Throughout the test, SVM has illustrated obvious advantage of overcoming regional support size effect even with limited labelling information. The Upscaling of permeability on the natural sandstone sample based on the rock type distribution has demonstrated excellent accuracy comparing with full scale direct computation.

Innovative data clustering method improves drought prediction in heterogeneous landscapes using GEE-derived remote sensing indices

Drought is a complex natural disaster characterized by unique features influenced by environmental factors, particularly at a regional scale. Remote Sensing (RS) indices have proven to be valuable in assessing drought conditions. In this study, we utilized the five most commonly used RS indices: Normalized Difference Vegetation Index (NDVI), Deviation/Anomaly of NDVI (NDVI-Dev/ANDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and 2-band Enhanced Vegetation Index (EVI2). These indices were extracted over a 30-year period (1988–2017) using Google Earth Engine (GEE) for modeling the Standardized Precipitation Index (SPI) with various time-spans (1, 3, 6, 9, 12, and 24 months) as the Ground-Based (GB) drought indices. The models were trained from 1988 to 2014 and tested on data from 2015 to 2017. To enhance RS-based drought modeling over the complex and diverse study area of Iran, we proposed two different data clustering methods based on distinct environmental factors – climatic condition and land-cover type – and compared the results. The first method employed an enhanced-climate-based approach to improve upon the standard-climate-based method. This approach considered variations in the environment caused by seasonal changes in different climatic zones, which had received less attention in previous studies. Unique Support Vector Machine (SVM) models were trained using various clusters of inputs/outputs to assess the efficiency of the RS indices in predicting different drought conditions under various circumstances. Furthermore, we introduced an innovative data clustering method based on RS-derived land-cover similarity, which resulted in further improvements in drought modeling compared to agroclimatic zoning. The fusion-based drought modeling approach, using all RS indices as inputs in the enhanced-climate-based and land-cover-based methods, demonstrated an aggregated Overall Accuracy (OA) of 92.93% and 95.11% for predicting SPI-3, respectively. Notably, within the land-cover-based method, TCI exhibited the highest aggregated performance compared to other RS indices in predicting SPI-3 with an OA of approximately 95%.

A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)

Big Data is an emerging technology with enormous potential to develop business and its administration. Due to the enormous volume, efficient data mining and clustering methods are crucial to extracting meaningful insights and patterns from large-scale datasets. Problems may arise from the need to analyze, capture, share, store, and visualize the data. Several methods have already been proposed for mining knowledge from big data. It is practically inefficient or impossible to handle these massive data using the proposed methods in a single machine because big data are frequently acquired from dispersed locations and stored on several machines. Matrix decomposition is one of the critical strategies to retrieve knowledge from diverse, noisy, huge data generated by modern applications and stored in dispersed locations. This study proposes a novel approach called the Rank-Revealing QR Matrix and Schur Decomposition Method (RRQR-SDM) specifically designed for big data mining and clustering tasks. The RRQR-SDM is designed to reveal the rank of the data matrix in a computationally efficient manner by using a modified QR decomposition, eliminating the need for expensive Singular Value Decomposition (SVD) computations. The proposed RRQR-SDM method offers several advantages over existing approaches. Firstly, exploiting the inherent low-rank structure reduces the computational complexity associated with large-scale datasets. By revealing the rank of the input matrix, it enables dimensionality reduction and efficient data compression. Secondly, the Schur decomposition enhances the interpretability of the data by providing a clear separation between the relevant and irrelevant components. This feature makes the RRQR-SDM method particularly suitable for data mining and clustering tasks where identifying the most significant features is essential. To evaluate the performance of the RRQR-SDM method, extensive experiments were conducted on various big data datasets. The results demonstrate that the proposed method outperforms state-of-the-art computational efficiency and clustering accuracy techniques.

Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles

This research introduces a stochastic scheduling approach that incorporates risk constraints for an energy hub (EH), considering uncertainties related to renewable generation and load demands. The proposed method utilizes the Conditional Value at Risk (CVaR) technique to assess and quantify risks. By striking a balance between reducing operational and emissions costs and increasing risk aversion, the approach presents a trade-off. The EH comprises various components such as a wind turbine (WT), photovoltaic (PV) cells, a fuel cell power plant (FCPP), a combined heat and power generation unit (CHP), and plug-in electric vehicles (PEVs). Uncertain variables encompass factors such as wind speed, solar irradiation, different demands, and market prices. To optimize profits and enhance the consumption curve, demand response programs (DRPs) for electrical, thermal, and cooling demands are implemented. To address the uncertainties associated with input random variables, the efficient k-means data clustering method is employed. A new slime mold algorithm, based on coughing and chaos theory, has been proposed to enhance the problem's solution. The algorithm incorporates innovative operators to improve its capabilities. By utilizing the coughing mechanism and chaos theory, the algorithm explores the solution space more effectively, resulting in improved outcomes for the problem at hand. The results demonstrate significant flexibility in EH management and are extensively discussed. Simulation results indicate that integrating PEVs, FCPP, and DRPs can lead to reductions of 2 %, 7 %, and 11 % in the EH's operating costs, respectively. Furthermore, considering PEVs, FCPP, and DRPs can improve the EH's risk cost by 1.98 %, 6.7 %, and 10.5 %, respectively. Based on the numerical results, in Case 4 led to a remarkable 12.65 % reduction in operational costs while simultaneously achieving a 15.43 % decrease in emission costs, showcasing the effectiveness of the proposed approach in optimizing energy management in an energy hub system.

Functional Data Clustering Via Functional Mahalanobis Distance

As an exploratory data analysis method, functional data clustering aims to identify the underlying features of the observed data. In this context, this paper proposes a functional data clustering method based on functional Mahalanobis distance. As a distance-based non-parametric clustering model, the proposed method can effectively avoid the disadvantages of generative models and has excellent properties of decoupling and dimension standardization. Compared with other functional data clustering models, this method has lower computational complexity. In addition, the proposed method can be applied to any distance-based multivariate clustering method, thus generalizing it to the case of functional data. In practical data analysis, this paper compares the performance of this method with some other functional clustering methods, using k-means clustering as an example, and finds that it has better performance in terms of purity, adjusted Rand index, and computational speed. Finally, the idea of using Mahalanobis distance for functional data distance measurement can also be extended to construct kernel functions for measuring similarity between functional data samples, thus developing non-linear functional data analysis methods based on reproducing kernel theory.

Attention-based deep clustering method for scRNA-seq cell type identification.

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

Machine Learning-Facilitated Cross-Panel Automatic Classification at Both Sample and Cell Level in Acute Myeloid Leukemia

Introduction Flow cytometry (FC) immunophenotyping is essential for accurate and prompt diagnosis of acute myeloid leukemia (AML), with up to 95% of AML patients showing detectable aberrant immunophenotype (IP) by FC. Current manual and subjective analysis of clinical FC data, however, lacks reproducibility and leads to interpretation variation. In previous studies, we showed that supervised machine learning (ML) approaches can detect IP abnormalities and identify the acute leukemia subclassification accurately at the specimen level. We also found that comparable classification performance can be achieved using only a subset of markers of the reagent panel (Ko et al., 2018; Monaghan et al., 2022). To interpret the ML classification results, we have developed data clustering and discriminative learning methods to identify and visualize the diagnostic cell populations in a 2D space for hematopathology review (Ji et al., 2020; Lee et al., 2018). Built upon these approaches, this study reports our findings on developing an interpretable cross-panel ML classification model to support timely and accurate diagnosis and subclassification of AML. Materials and Method The AML diagnosis model at the sample level utilized FC data obtained from 53 bone marrow (BM) aspirate samples from Roswell Park Comprehensive Cancer Center (RPCCC) and 50 BM aspirate samples from UPMC. Data from RPCCC were acquired using a Beckman Coulter Navios EX and measured with the ClearLLab10C panel; while data from UPMC were acquired using a BD FACSCantoII and measured with a UPMC-developed diagnostic panel. We used only the common parameters collected within these 103 FC data sets and applied a previously published Gaussian Mixture Model-Support Vector Machine (GMM-SVM) based approach to train a cross panel sample-level classification model for AML diagnosis (Ko et al., 2018; Monaghan et al., 2022). Three-fold cross-validation was conducted to assess the model performance, using sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC). For the cell-level visualization and analysis, all samples in the training set were pooled together, before a uniform manifold approximation and projection (UMAP) process was applied to transform and visualize the cell populations within the samples. Specifically, 26 non-neoplastic and 27 AML samples from the M2 panel of the ClearLLab10C from the RPCCC cohort were combined for the UMAP visualization. Then the FlowSOM unsupervised clustering method and the DAFI semi-supervised clustering method (Lee et al., 2018) were applied to identify all of the cell populations that have significant differences between the AML samples and the non-neoplastic controls. Results The cross-panel AML versus non-neoplastic classifier, combining FC data from UPMC and RPCCC achieved exceptional performance. The AML detection model using all parameters on the ClearLLab10C panel achieved an AUC of 100%. When using only the overlapping parameters between the ClearLLab10C and the UPMC panels, the classification model achieved an AUC of 99.5%, while the cross-panel classifier achieved an AUC of 99.1% (Table 1). Specimen-level visualization confirmed the separation of AML versus non-neoplastic controls. The UMAP plots, created by pooling non-neoplastic and AML samples, exhibited distinct patterns for different AML subtypes (M1 to M5) and a genetic mutation (e.g., AML IDH1), which were distinct from control samples. Using the automated gating analysis, cell populations identified as different between AML and control samples were characterized and visualized in 2D-by-2D scatter plots for straightforward interpretation and assessment based on their immunophenotype. Conclusion Our study demonstrated the feasibility to develop and assess a ML classification model for AML detection and diagnosis using overlapping markers in FC data across reagent panels and sites. Advanced visualization and automated gating analysis enabled the interpretation of the ML classification by identifying and characterizing the diagnostic cell populations and their phenotypic heterogeneity, providing insights into AML heterogeneity at both the specimen and the single cell level. These findings highlight the potential of ML capability for improving patient care by timely and accurate diagnosis and subclassification of AML.

Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors

BackgroundTechnology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-up is often small. Such data allow explorations of the role of intra-person variability in understanding disease etiology and characterizing disease processes. A specific example was to characterize pathogenesis of bacterial vaginosis (BV) using weekly vaginal microbiota Nugent assay scores collected over 2 years in post-menarcheeal women from Rakai, Uganda, and to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV.MethodsWe use a fully data-driven approach to characterize the longitudinal patters of vaginal microbiota by considering the densely sampled Nugent scores to be random functions over time and performing dimension reduction by functional principal components. Extending a current functional data clustering method, we use a hierarchical functional clustering framework considering multiple data features to help identify clinically meaningful patterns of vaginal microbiota fluctuations. Additionally, multinomial logistic regression was used to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV.ResultsUsing weekly Nugent scores over 2 years of 211 sexually active and post-menarcheal women in Rakai, four patterns of vaginal microbiota variation were identified: persistent with a BV state (high Nugent scores), persistent with normal ranged Nugent scores, large fluctuation of Nugent scores which however are predominantly in the BV state; large fluctuation of Nugent scores but predominantly the scores are in the normal state. Higher Nugent score at the start of an interval, younger age group of less than 20 years, unprotected source for bathing water, a woman’s partner’s being not circumcised, use of injectable/Norplant hormonal contraceptives for family planning were associated with higher odds of persistent BV in women.ConclusionThe hierarchical functional data clustering method can be used for fully data driven unsupervised clustering of densely sampled longitudinal data to identify clinically informative clusters and risk-factors associated with each cluster.

Anomali Data Mining Menggunakan Metode K-Means Dalam Penilaian Mahasiswa Terhadap Pelayanan Prodi

Cluster analysis is a data mining technique that aims to identify a group of objects that have the same characteristics. The number of groups that can be identified depends on the amount of data and the type of object, so that data problems arise when there is a change to a number of redundant data, but not all of it is changed where the data above is repeatedly made into one table with a different code as the primary key and there are anomalies Insertion, so K-means is one method of clustering data which is divided into the form of one or more clusters/groups that have the same characteristics. Student data clustering uses the k-means method, consisting of student assessments. This study uses student assessment data. Then it was concluded that the assessment group was based on reliability aspects: the ability of lecturers, education staff and administrators to provide services, responsiveness aspects: the willingness of lecturers, education staff and administrators to help students and provide services quickly, aspects of certainty ( assurance): the ability of lecturers, staff and administrators to give confidence to students that the services provided are in accordance with the provisions, aspects of empathy (empathy): the willingness/concern of lecturers, staff and managers to give attention to students, tangibles aspects: students' assessment of the adequacy , accessibility, quality of facilities and infrastructure from the grouping results based on reliability, responsiveness, assurance and empathy data.

A Novel Curve Clustering Method for Functional Data: Applications to COVID-19 and Financial Data

Functional data analysis has significantly enriched the landscape of existing data analysis methodologies, providing a new framework for comprehending data structures and extracting valuable insights. This paper is dedicated to addressing functional data clustering—a pivotal challenge within functional data analysis. Our contribution to this field manifests through the introduction of innovative clustering methodologies tailored specifically to functional curves. Initially, we present a proximity measure algorithm designed for functional curve clustering. This innovative clustering approach offers the flexibility to redefine measurement points on continuous functions, adapting to either equidistant or nonuniform arrangements, as dictated by the demands of the proximity measure. Central to this method is the “proximity threshold”, a critical parameter that governs the cluster count, and its selection is thoroughly explored. Subsequently, we propose a time-shift clustering algorithm designed for time-series data. This approach identifies historical data segments that share patterns similar to those observed in the present. To evaluate the effectiveness of our methodologies, we conduct comparisons with the classic K-means clustering method and apply them to simulated data, yielding encouraging simulation results. Moving beyond simulation, we apply the proposed proximity measure algorithm to COVID-19 data, yielding notable clustering accuracy. Additionally, the time-shift clustering algorithm is employed to analyse NASDAQ Composite data, successfully revealing underlying economic cycles.

Finding Playing Styles of Badminton Players Using Firefly Algorithm Based Clustering Algorithms

Cluster analysis can be defined as applying clustering algorithms with the goal of finding hidden patterns or groupings in a dataset. Different clustering methods provide different solutions for the same dataset. Traditional clustering algorithms are popular, but handling big data sets is beyond the ability of such methods. We propose three big data clustering methods, based on the Firefly Algorithm (FA). Three different fitness functions were defined on FA using inter cluster distance, intra cluster distance, silhouette value and Calinski-Harabasz Index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with four popular synthetic data sets and later applied on two badminton data sets to identify different playing styles of players based on physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work similarly as the APSO method and surpass the performance of traditional algorithms.

Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods.

In the present study, various combinations of dimensionality reduction methods with data clustering methods for the analysis of biopsy samples of intracranial tumors were investigated. Fresh biopsies of intracranial tumors were studied in the Laboratory of Neurosurgical Anatomy and Preservation of Biological Materials of N.N. Burdenko Neurosurgery Medical Center no later than 4 h after surgery. The spectra of Protoporphyrin IX (Pp IX) fluorescence, diffuse reflectance (DR) and Raman scattering (RS) of biopsy samples were recorded. Diffuse reflectance studies were carried out using a white light source in the visible region. Raman scattering spectra were obtained using a 785 nm laser. Patients diagnosed with meningioma, glioblastoma, oligodendroglioma, and astrocytoma were studied. We used the cluster analysis method to detect natural clusters in the data sample presented in the feature space formed based on the spectrum analysis. For data analysis, four clustering algorithms with eight dimensionality reduction algorithms were considered.

Hypergraph-Clustering Method Based on an Improved Apriori Algorithm

With the complexity and variability of data structures and dimensions, traditional clustering algorithms face various challenges. The integration of network science and clustering has become a popular field of exploration. One of the main challenges is how to handle large-scale and complex high-dimensional data effectively. Hypergraphs can accurately represent multidimensional heterogeneous data, making them important for improving clustering performance. In this paper, we propose a hypergraph-clustering method dubbed the “high-dimensional data clustering method” based on hypergraph partitioning using an improved Apriori algorithm (HDHPA). First, the method constructs a hypergraph based on the improved Apriori association rule algorithm, where frequent itemsets existing in high-dimensional data are treated as hyperedges. Then, different frequent itemsets are mined in parallel to obtain hyperedges with corresponding ranks, avoiding the generation of redundant rules and improving mining efficiency. Next, we use the dense subgraph partition (DSP) algorithm to divide the hypergraph into multiple subclusters. Finally, we merge the subclusters through dense sub-hypergraphs to obtain the clustering results. The advantage of this method lies in its use of the hypergraph model to discretize the association between data in space, which further enhances the effectiveness and accuracy of clustering. We comprehensively compare the proposed HDHPA method with several advanced hypergraph-clustering methods using seven different types of high-dimensional datasets and then compare their running times. The results show that the clustering evaluation index values of the HDHPA method are generally superior to all other methods. The maximum ARI value can reach 0.834, an increase of 42%, and the average running time is lower than other methods. All in all, HDHPA exhibits an excellent comparable performance on multiple real networks. The research results of this paper provide an effective solution for processing and analyzing large-scale network datasets and are also conducive to broadening the application range of clustering techniques.

Unveiling elemental fingerprints: A comparative study of clustering methods for multi-element nanoparticle data

Single particle-inductively coupled plasma-time of flight-mass spectrometers (SP-ICP-TOF-MS) generates large datasets of the multi-elemental composition of nanoparticles. However, extracting useful information from such datasets is challenging. Hierarchical clustering (HC) has been successfully applied to extract elemental fingerprints from multi-element nanoparticle data obtained by SP-ICP-TOF-MS. However, many other clustering approaches can be applied to analyze SP-ICP-TOF-MS data that have not yet been evaluated. This study fills this knowledge gap by comparing the performance of three clustering approaches: HC, spectral clustering, and t-distributed Stochastic Neighbor Embedding coupled with Density-Based Spatial Clustering of Applications with Noise (tSNE-DBSCAN) for analyzing SP-ICP-TOF-MS data. The performance of these clustering techniques was evaluated by comparing the size of the extracted clusters and the similarity of the elemental composition of nanoparticles within each cluster. Hierarchical clustering often failed to achieve an optimal clustering solution for SP-ICP-TOF-MS data because HC is sensitive to the presence of outliers. Spectral clustering and tSNE-DBSCAN extracted clusters that were not identified by HC. This is because spectral clustering, a method developed based on graph theory, reveals the global and local structure in the data. tSNE reduces and maps the data into a lower-dimensional space, enabling clustering algorithms such as DBSCAN to identify subclusters with subtle differences in their elemental composition. However, tSNE-DBSCAN can lead to unsatisfactory clustering solutions because tuning the perplexity hyperparameter of tSNE is a difficult and a time-consuming task, and the relative distance between datapoints is not maintained. Although the three clustering approaches successfully extract useful information from SP-ICP-TOF-MS data, spectral clustering outperforms HC and tSNE-DBSCAN by generating clusters of a large number of nanoparticles with similar elemental compositions.