Data Clustering Method Research Articles

A recognition method of learning behaviour in English online classroom based on feature data mining

This paper proposes a recognition method of learning behaviour in English online classroom based on feature data mining. Firstly, with the support of fractal theory, the adjacent search method is used to extract the edge of learning behaviour image, and then the data clustering method is used to reduce the dynamic change range of data caused by edge extraction and improve the degree of data standardisation. Finally, the optimal characteristics of learning behaviour are obtained by Drosophila optimisation algorithm, then the learning behaviour recognition of English online classroom is realised by mining characteristic data. Simulation results show that this method has the highest accuracy of 98% and the comprehensiveness of recognition of different types of learning behaviour can reach 0.95. This method retains the details of behaviour image as much as possible to make it more practical.

A prediction method of soil environmental pollutants in landscape architecture planning based on data clustering

Aiming at the problem of low prediction accuracy in the prediction of soil environmental pollutants in landscape architecture, a prediction method of soil environmental pollutants in landscape architecture planning based on data clustering is designed. Firstly, through the soil pollution evaluation standard in landscape architecture planning, the pollution index system is constructed to obtain the pollution index data. Then, the consistency pre-processing of the obtained pollution index data is carried out, and the data features are extracted with the help of regionalised variables and variogram. Finally, according to the feature extraction results, the pollution weight is determined by Nemero index, and the pollution grade is divided by data clustering method. According to the pollution trend, the prediction model of pollutant soil pollution degree is constructed to complete the prediction. The results show that the proposed method can effectively improve the prediction accuracy of soil pollution.

Comparative analysis of experimental data clustering in MATLAB and Python environment

The paper compares normalization and clustering methods for processing experimental data in the MATLAB package and a Python program using the scikit-learn library. Recommendations on further application of Python programs and selection of normalization and clustering methods are provided. The choice of clustering methods for further research is discussed.

Optimized Classification Model for Biomedical Data Analysis

In recent years, medical data analysis has become a well-known research topic in healthcare in recent years. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things, cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient to support healthcare practitioners in their decision-making. Due to the huge amount of medical data, appropriate clustering methods have been proven useful to implement an efficient medical data classification process. In addition, the development of machine learning models effectively aids the classification process. With this motivation, this paper focuses on a unit-wide search algorithm with an optimal support vector machine model for medical data analysis. The purpose of the proposed model is to classify health data using clustering and classification models. In addition, the proposed technique includes a clustering method for medical data, which helps to improve the classification performance. In addition, the model used examines accumulated medical data to perform the classification process. In addition, an optimization algorithm is used to improve the classification results of medical data. Extensive benchmarking and a wide range of simulations can highlight the promising performance of the research technique.

Аналіз рішень та підходів кластеризації геопросторових даних для оптимізації продуктивності веб-карти та взаємодії користувача

In the contemporary epoch, the management and visualization of geospatial information in web browsers have gained substantial importance. Web maps are indispensable tools across various tourism, goods delivery, and ecology sectors. Furthermore, the extensive support of web browsers on diverse devices enhances the accessibility of geospatial data on the web for various users. However, the incessant increment of geospatial information poses new challenges in efficiently displaying data and navigation through these data on web maps. Therefore, the clustering of geospatial data is crucial in dealing with them. Different clustering methods may affect the performance or visual clarity of web maps. To improve the user experience and optimize the use of computing resources, geodata clustering becomes a necessary tool for processing large volumes of markers on the map. Despite significant progress in the development of geodata clustering solutions in web maps, there are some challenges that developers and users may encounter. In this article, challenges with scaling, dynamic cluster data, and heterogeneity of data are described. Existing problems in geodata clustering in web maps require additional research and development. Understanding these issues will help developers and researchers improve existing solutions and create new methods and approaches for efficient clustering of geodata in web maps. The urgency of solving the problem lies in the search for effective clustering solutions that provide an opportunity to ensure convenient interactivity and fast processing of geodata in web maps. This study provides a comprehensive review of data types and clustering methods. Tools and libraries for geodata clustering in web maps are analyzed. Different types of geodata and approaches to working with them were also studied. Concepts such as semi-static data and their positions alongside static and dynamic data types are elucidated. Through the analysis, optimal scenarios for applying specific clustering methods or the utilization of server-side clustering approaches have been identified. Conclusions have also been drawn on the preferred approach when handling extensive volumes of static or semi-static geospatial data, particularly advocating for the application of server-side clustering with caching. In conclusion, various clustering approaches in web maps, both client-side and server-side, have been scrutinized. The advantages and disadvantages of both approaches, along with recommendations on when to apply each method, have been delineated. A noticeable absence of explicit approaches in clustering vast geospatial data for web map representation underpins the relevance and necessity of research in this direction.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.

Study on the Development and Implementation of Different Big Data Clustering Methods

Study on the Development and Implementation of Different Big Data Clustering Methods

Simulation Of The K-Means Clustering Algorithm With The Elbow Method in Making Clusters Of Provincial Poverty Levels in Indonesia

One way to ensure that government programs and assistance for each province are right on target is to create a model of grouping or clustering provinces in Indonesia based on poverty levels. Algorithm K Means is one of the clustering methods in Data Mining to divide n observations into k groups so that each observation is in the group with the closest mean. In this study, provincial poverty level clustering in Indonesia will be made based on three poverty level indicators, namely the Percentage of Poor Population (P0), Poverty Depth (P1), and Poverty Severity (P2) with the K-Means Algorithm using the Elbow Method assisted by the Python Program. The results obtained are 5 optimal clusters of provincial poverty rates in Indonesia.

Knowledge‑Intensive Business Services Employment Structure and Economic Development in EU Regions

The study presents the results of grouping EU NUTS 2 regions based on the share of employment in particular sectors (knowledge‑intensive high‑technology services, knowledge‑intensive market services and other knowledge‑intensive services), as well as GDP per capita, in 2008 and 2018. The grouping of regions was done by clustering methods (for structure data), including Ward’s method to determine the number of groups and the k‑means for the final partition. GDP groups were defined using a sample mean and one standard deviation. To assess the similarity of the classifications and, consequently, to evaluate correlations between the employment structures and the level and pace of economic development, the similarity measure for partitions proposed by Sokołowski was used.

A Decision-Making Approach for Sustainable Machining Processes Using Data Clustering and Multi-Objective Optimization

Achieving sustainable machining processes has become crucial in many industries in order to support sustainable development goals (e.g., good health and well-being, decent work and economic growth, affordable and clean energy). Many attempts have been made to optimize the sustainability aspect during machining processes and to offer optimized cutting conditions. However, there is a vital need to develop a decision-making approach that can be flexible and offer optimal sustainable solutions for different machining scenarios. The current study offers a new decision-making approach for sustainable machining processes using data clustering (i.e., K-means clustering) and multi-objective optimization methods (i.e., grey relational analysis). Utilizing the multi-objective optimization after the clustering phase provides the decision maker with optimal and sustainable cutting conditions for different clusters. The developed approach is validated through a case study that includes five design variables (i.e., feed, speed, nose radius, cooling strategy, and rake angle), three machining outputs (i.e., surface roughness, specific energy, and unit volume machining time), and four different scenarios (i.e., finishing, roughing, balanced, and entropy). Three clusters were generated, and the obtained results were compatible with the physical meaning of each studied scenario. Such an approach can provide the decision maker with sufficient flexibility to select the optimal cutting settings for various scenarios, as well as the freedom to switch between clusters and/or scenarios with minimal effort.

Dynamic Nonparametric Clustering of Multivariate Panel Data

Abstract We introduce a new dynamic clustering method for multivariate panel data characterized by time-variation in cluster locations and shapes, cluster compositions, and possibly the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations toward the current center of their previous cluster assignment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020.

Analysis of The Theme Clustering Algorithm Using K-Means Method

The title of this research is the analysis of the thesis theme clustering algorithm using the k-means method. The main problem is how we can find out which theme is most in demand by thesis students at the Faculty of Engineering, University of Muhammadiyah Bengkulu. This clustering uses the K-means method. The K-Means method was chosen because this method is one of the non-hierarchical data clustering methods that seeks to partition data into two or more clusters with the same characteristics included in the same cluster. The purpose of this research is to help prospective students who will write their thesis in knowing which themes are more interested in them.

ScSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder

Single cell sequencing is a technology for high-throughput sequencing analysis of genome, transcriptome and epigenome at the single cell level. It can improve the shortcomings of traditional methods, reveal the gene structure and gene expression state of a single cell, and reflect the heterogeneity between cells. Among them, the clustering analysis of single-cell RNA data is a very important step, but the clustering of single-cell RNA data is faced with two difficulties, dropout events and dimension curse. At present, many methods are only driven by data, and do not make full use of the existing biological information. In this work, we propose scSSA, a clustering model based on semi-supervised autoencoder, fast independent component analysis (FastICA) and Gaussian mixture clustering. Firstly, the semi-supervised autoencoder imputes and denoises the scRNA-seq data, and then get the low-dimensional latent representation. Secondly, the low-dimensional representation is reduced the dimension and clustered by FastICA and Gaussian mixture model respectively. Finally, scSSA is compared with Seurat, CIDR and other methods on 10 public scRNA-seq datasets. The results show that scSSA has superior performance in cell clustering on 10 public datasets. In conclusion, scSSA can accurately identify the cell types and is generally applicable to all kinds of single cell datasets. scSSA has great application potential in the field of scRNA-seq data analysis. Details in the code have been uploaded to the website https://github.com/houtongshuai123/scSSA/.

Analysis of human capital development in Russia by means of physical culture and sports using neural network modeling

Subject. The article investigates the human capital in the regions of the Russian Federation in the context of health promotion and active longevity of the population of the country. Objectives. The purpose is to examine the state of human capital driven by the development of physical culture and sports in Russian regions, using neural network modeling. Methods. Neural networks are used as a method of studying the multidimensional statistical data. The method of data clustering based on neural network modeling is not affiliated with model constraints. This method is aimed at self–training of neural networks, i.e. self-organizing Kohonen maps, and is a promising means of visual representation of multidimensional data space. Results. Using the neural network modeling, we placed 85 Russian regions in six cluster formations. We assessed the influence of each studied indicator on cluster construction. The paper demonstrates a strong difference in the number of regions of the Russian Federation in clusters, presents average values of the considered indicators in clusters for 2021. Conclusions. The study shows uneven development of human capital in Russian regions from the point of view of physical culture and sports, which predetermines different strategies for the development of the regional sports sphere. The positive trend in the number of sports facilities, personnel, and population engaged in physical culture and sports is facilitated by a systems approach based on program documents, which focuses on strengthening the health of citizens and their active longevity.

Imputation Strategies for Clustering Mixed-Type Data with Missing Values

Incomplete data sets with different data types are difficult to handle, but regularly to be found in practical clustering tasks. Therefore in this paper, two procedures for clustering mixed-type data with missing values are derived and analyzed in a simulation study with respect to the factors of partition, prototypes, imputed values, and cluster assignment. Both approaches are based on the k-prototypes algorithm (an extension of k-means), which is one of the most common clustering methods for mixed-type data (i.e., numerical and categorical variables). For k-means clustering of incomplete data, the k-POD algorithm recently has been proposed, which imputes the missings with values of the associated cluster center. We derive an adaptation of the latter and additionally present a cluster aggregation strategy after multiple imputation. It turns out that even a simplified and time-saving variant of the presented method can compete with multiple imputation and subsequent pooling.

Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation.

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.

Cancer tissue detection using improved K-means initialization method for multi-dimensional microarray big data clustering

Cancer tissue detection using improved K-means initialization method for multi-dimensional microarray big data clustering

A network-constrained clustering method for bivariate origin-destination movement data

For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services.

Preserving bilateral view structural information for subspace clustering

Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.

A review and performance evaluation of clustering frameworks for single-cell Hi-C data.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.