Clustering Model Research Articles

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

High Prevalence of Laxative Use Among Those Tested for Clostridioides difficile Infection in VA Hospitals

Background: Clostridioides difficile infection (CDI) is associated with 500,000 infections and 30,000 deaths per year. Inappropriate testing and treatment of patients with asymptomatic colonization occurs frequently (between 15% and 41%). The VA CDI guidelines emphasize avoidance of CDI testing in patients with laxative use within the previous 48 hours due to the high likelihood of non-infectious diarrhea. The objective of this study was to assess laxative administration among inpatients tested for CDI in VA hospitals and identify factors associated with guideline discordance. Methods: Adults hospitalized in Illinois, Wisconsin, and Michigan VA Medical Centers from January 2019-December 2022 with a CDI test performed during the admission were included. CDI tests included Toxin B gene Polymerase Chain Reaction or Toxin Enzyme Immunoassay. Tests were defined as positive, negative, or cancelled according to the diagnostic protocols of the VA testing laboratories. Laxative use, patient demographics, admission data, and comorbidities were collected from the VA Corporate Data Warehouse. Guideline discordant testing was defined as a diagnostic test for CDI ordered within 48 hours of a recorded laxative dose. Factors associated with discordant testing were analyzed using clustered binomial logistic regression models. Analyses were completed using SAS 9.4. Results: There were 7,326 tests ordered for 4,888 patients during the study. Patients were predominantly White (61.8%), male (95.6%), and elderly (mean age=70.0 standard deviation=12.1). Most (59.0%) patients had received at least one dose of laxative in the 48 hours preceding their CDI test. Being Black (Odds Ratio (OR)=0.86 (95%Confidence Interval (95%CI) =0.76,0.98) or Hispanic (OR (95%CI) =0.62(0.48,0.82) vs White) was associated with a decreased likelihood of inappropriate testing due to recent laxative use. Being tested at a rural facility (OR (95%CI) =1.23 (1.07,1.41) vs urban), within a long-term care (LTC) unit (OR (95%CI) =1.67 (1.41,1.97) vs inpatient), or within an intensive care unit (ICU) (OR (95%CI) =1.40 (1.24,1.59)) were all associated with an increased likelihood of being inappropriately tested. Guideline discordant tests were more likely to have negative results (OR (95%CI) =1.25 (1.05,1.49)) compared to guideline concordant tests. Discussion: Laxative administration in the 48 hours preceding CDI testing was common among hospitalized Veterans and associated with a lower likelihood of positive Results: This echoes non-VA studies where laxative use was reported at 44%. An increased likelihood of guideline discordant testing in ICU and LTC settings suggests the need for greater diagnostic stewardship interventions. Additionally, further work to determine negative outcomes associated with inappropriate testing are needed.

Fast Clustering via Maximizing Adaptively Within-Class Similarity.

Clustering aims to make data points in the same group have higher similarity or make data points in different groups have lower similarity. Therefore, we propose three novel fast clustering models motivated by maximizing within-class similarity, which can obtain more instinct clustering structure of data. Different from traditional clustering methods, we divide all n samples into m classes by the pseudo label propagation algorithm first, and then m classes are merged to c classes ( ) by the proposed three co-clustering models, where c is the real number of categories. On the one hand, dividing all samples into more subclasses first can preserve more local information. On the other hand, proposed three co-clustering models are motivated by the thought of maximizing the sum of within-class similarity, which can utilize the dual information between rows and columns. Besides, the proposed pseudo label propagation algorithm can be a new method to construct anchor graphs with linear time complexity. A series of experiments are conducted on both synthetic and real-world datasets and the experimental results show the superior performance of three models. It is worth noting that for the proposed models, FMAWS2 is the generalization of FMAWS1 and FMAWS3 is the generalization of other two.

Bidirectional Probabilistic Subspaces Approximation for Multiview Clustering.

The existing multiview clustering models learn a consistent low-dimensional embedding either from multiple feature matrices or multiple similarity matrices, which ignores the interaction between the two procedures and limits the improvement of clustering performance on multiview data. To address this issue, a bidirectional probabilistic subspaces approximation (BPSA) model is developed in this article to learn a consistently orthogonal embedding from multiple feature matrices and multiple similarity matrices simultaneously via the disturbed probabilistic subspace modeling and approximation. A skillful bidirectional fusion strategy is designed to guarantee the parameter-free property of the BPSA model. Two adaptively weighted learning mechanisms are introduced to ensure the inconsistencies among multiple views and the inconsistencies between bidirectional learning processes. To solve the optimization problem involved in the BPSA model, an iterative solver is derived, and a rigorous convergence guarantee is provided. Extensive experimental results on both toy and real-world datasets demonstrate that our BPSA model achieves state-of-the-art performance even if it is parameter-free.

AN AUTOMATED APPROACH TO SELECTING SENTENCES FOR TEST CASE GENERATION

The modern field of education is characterized by the increasing use of multiple choice tests to assess students’ knowledge and skills. One of the common methods of selecting sentences for such tests is the application of textual data clustering procedures. In this study, a module for sentence selection was developed that includes three steps: preprocessing, sentence parameter computation, and clustering. However, an objective evaluation of the quality of the obtained clusters using the silhouette coefficient and Davis-Boldin index showed that the clustering model used did not give satisfactory results.

A Novel Grey Fuzzy Clustering Model and its Application on Students’ Performance Evaluation

Aiming to address the issues of traditional student evaluation methods, which tend to be overly subjective and overlook the intrinsic data structure, this paper introduces a novel grey fuzzy clustering model named the grey entropy game-weighted fuzzy c-means (GEG-WFCM) model. Firstly, subjective weights are calculated using the subjective-objective relationship analysis method, while objective weights are determined through the entropy weight method. Then, a comprehensive approach is adopted, leveraging game theory to calculate the final weights. Based on these comprehensive weights, the relative grey correlation coefficient and fuzzy weighted c-mean algorithm are incorporated to yield the ultimate evaluation results. The proposed model was applied to evaluate student performance, and the experiments show that it can obtain scientific and reasonable results. The model not only acknowledges the expertise of experts but also respects the objectivity of the data, thus circumventing the limitations of purely subjective judgments, and surpassing traditional evaluation methods.

Implementasi Pembelajaran PAI pada Sekolah Inklusi di Sekolah Inklusi SDN Mentikan 1 Mojokerto

Inclusive schools play a crucial role in accepting all students, including those with special needs (ABK). However, not all inclusive schools successfully implement this concept effectively. This research focuses on SDN Mentikan 1 Mojokerto as an example of an inclusive school, where ABK and regular students learn together with adapted teaching. Specifically, the study explores the planning, implementation, and evaluation of Islamic Education (PAI) learning in the 4th grade. Qualitative methods and a case study approach were utilized, with data collected through interviews, observations, and documentation. The findings indicate that PAI teachers use the same curriculum for both regular and ABK students, implementing full inclusion, clustering, and pull-out models in ABK learning services. The PAI learning process in the 4th grade involves joint participation of ABK and regular students, in accordance with the prepared lesson plans. The evaluation of PAI learning encompasses cognitive, affective, and psychomotor aspects of ABK, using methods such as exams, daily assessments, projects/products, and other evaluations. Remedial actions are taken as needed during the evaluation. This research provides an overview of inclusive PAI at SDN Mentikan 1, highlighting challenges and potential improvements to support the optimal development of all students, especially ABK, in the context of inclusive schooling. The study aims to understand the supporting factors and obstacles in implementing PAI learning in inclusive schools.

Monocular visual anti-collision method based on residual mixed attention for storage and retrieval machines

In the traditional manufacturing industry, the safe operation of storage and retrieval (S/R) machines is vital for efficient automated warehouse management. Recently, deep learning-based monocular vision approaches have emerged as a promising solution for collision avoidance in automated storage and retrieval systems (AS/RS). However, the scarcity of specific datasets and the need for high-performance, lightweight neural network models pose challenges in the AS/RS domain. To address these challenges, this paper introduces a novel workflow for implementing a monocular anti-collision system for S/R machines. A semi-supervised annotation method leveraging a pretrained clustering model is proposed to expedite image collection and classification, resulting in a tailored dataset. Furthermore, the NightMix technique is introduced, combining Grayscale Enhancement and Floating Grid Mix method to enhance dataset diversity and capacity. Notably, a residual attention awareness module is integrated into MobileNet V3 to enhance its feature processing capabilities. Finally, the proposed approaches are validated through extensive experiments involving various design choices. The experimental results demonstrate that our methods are not only computationally efficient but also highly competitive in terms of learning performance.

A Measurement Method for Body Parameters of Mongolian Horses Based on Deep Learning and Machine Vision

The traditional manual methods for measuring Mongolian horse body parameters are not very safe, have low levels of automation, and cannot effectively ensure animal welfare. This research proposes a method for extracting target Mongolian horse body parameters based on deep learning and machine vision technology. Firstly, Swin Transformer is used as the backbone feature extraction network of Mask R-CNN model, and the CNN-based differentiated feature clustering model is added to minimize the loss of similarity and spatial continuity between pixels, thereby improving the robustness of the model while reducing error pixels and optimizing the rough mask boundary output. Secondly, an improved Harris algorithm and a polynomial fitting method based on contour curves are applied to determine the positions of various measurement points on the horse mask and calculate various body parameters. The accuracy of the proposed method was tested using 20 Mongolian horses. The experimental results show that compared with the original Mask R-CNN network, the PA (pixel accuracy) and MIoU (mean intersection over union) of the optimized model results increased from 91.46% and 84.72% to 98.72% and 95.36%, respectively. The average relative errors of shoulder height, withers height, chest depth, body length, croup height, shoulder angle, and croup angle were 4.01%, 2.98%, 4.86%, 2.97%, 3.06%, 4.91%, and 5.21%, respectively. The research results can provide technical support for assessing body parameters related to the performance of horses under natural conditions, which is of great significance for improving the refinement and welfare of Mongolian horse breeding techniques.

The spatial imaginaries of creative clusters: Examining the role of R&D programmes in creative placemaking

Creative clusters – geographic concentrations of creative industries sector activity, its skilled individuals, organisations and institutions – have attracted significant investment globally, becoming an important driver of economic growth. In this paper, we frame investment in creative clusters as a mechanism for creative placemaking. Creative placemaking occupies a dual role as a driver of economic development via arts-led ‘regeneration’ of high streets and flagship infrastructural projects like cultural quarters, yet also as a sustained stewarding of creative places through social engagement and community-centred decision-making about cultural projects. This distinction is typically framed as ‘top-down’ versus ‘bottom-up’ creative placemaking. We use the Bristol+Bath Creative R+D (BBCRD) programme as a case study to show how this distinction becomes less black and white. BBCRD created a ‘twin city’ spatial imaginary distinct from the geography of existing cultural or placemaking policy remits. The novel contribution of this paper is in how it evidences the intersecting scales of creative placemaking and unpacks the effectiveness of multi-city regionality for creative clustering, using fine-grained empirical data on the impact of top-down placemaking initiatives for the existing creative ecology of a place. This type of data and analysis is largely missing from literature on both clustering and placemaking. Given the continued international replication of the clusters model, recently renewed via ‘supercluster’ and ‘creative corridor’ discourses, we propose that an ecological understanding − that takes place specificity, relationality and scale into consideration – is pressing, and offers a route for complementarity between top-down and bottom-up creative placemaking.

Bayesian Statistical Inference for Factor Analysis Models with Clustered Data

Clustered data are a complex and frequently used type of data. Traditional factor analysis methods are effective for non-clustered data, but they do not adequately capture correlations between multiple observed individuals or variables in clustered data. This paper proposes a Bayesian approach utilizing MCMC and Gibbs sampling algorithms to accurately estimate parameters of interest within the clustered factor analysis model. The mean traversal graph of parameters ensures that the Markov chain converges, and the Bayesian case-deletion model is used to analyze the model’s impact and identify outliers in clustered data using Cook’s posterior mean distance. The applicability and validity of the principal-component-method-based factor analysis model for clustered data are demonstrated by comparing the parameter estimation of this method with the principal component method, the clustered data with and without internal relationships are compared by example analysis, and the anomalous groups are identified by the Cook’s posterior mean distance.

Development of Competitive Strategies in the "Clarinet" (Clustering and Networking) Model in Private Schools to Increase Student Interest

This research aims to describe the framework for developing competitive strategies for schools using “Clarinet” (clustering and networking) models, as well as to assess the feasibility of the development guide for competitive strategies in “Clarinet” models. This research uses a qualitative approach with the Research and Development (R&D) method by Borg and Gall. Data collection is conducted through questionnaires, documentation studies, in-depth interviews, and Focus Group Discussions (FGD). Data collection is done through documentation studies, interviews, questionnaires, and focus group discussions. The data analysis is conducted through the triangulation of techniques and sources. The results show that the validation test for the feasibility of the model and competitive strategies by education management experts yielded 88%, and marketing management experts yielded 96%. The research results indicate that the trial of the competitive strategy product using clustering and networking models is approximately 94% (very good/very valid). It is concluded that the competitive strategy within clustering and networking models is worthy of implementation to enhance the interest of students.

A hybrid machine learning approach for congestion prediction and warning

ABSTRACT Global traffic management encounters a significant challenge in traffic congestion. This paper presents a hybrid machine learning method for predicting traffic congestion. It leverages Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) network for parameter prediction and combines clustering and classification models, using the K-means algorithm to categorize traffic states into four levels and constructing a KNN classification model based on this segmentation. This results in the K-means-KNN model. Predicted parameters are inputted into the K-means-KNN model for congestion level prediction. Validation with real traffic flow data shows that the CNN-GRU network can capture spatiotemporal features more effectively. For instance, in traffic flow prediction, it reduces the MAPE by 7.39% and 51.14% compared to CNN and GRU, respectively. K-means-KNN excels in traffic state discrimination, achieving a congestion prediction accuracy of 91.8%. These results underscore the efficacy of the hybrid machine learning method in assessing and predicting urban traffic congestion.

Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning features

The importance of rumor detection on social media is self-evident. However, many existing studies have focused on exploring potential features in text content and propagation patterns, while neglecting a key aspect—the explainability of the model. The comment content can provide support for the credibility of the detection. Nevertheless, most studies that use comments encode them into specific models, rarely considering their semantic attitudes and standpoints, making it difficult for models to explain why a post is a rumor. In this study, we propose an Explainable rumor detection model based on Grey clustering called MDE-Grey, which combines Manual features and Deep learning features. In terms of manual features, we constructed a relevant vocabulary based on the specific comment environment of rumors to capture comment standpoints. In terms of deep learning features, we have designed a GCN sub network that includes two attention mechanisms to capture noteworthy content in posts and comments. Finally, we constructed a new grey clustering model to fuse the two types of features and obtain the final prediction. In the grey clustering model, we designed new whitening functions to capture the intrinsic relationship between features and rumor categories, ensuring the traceability of prediction results. The experiments on three datasets and case studies have demonstrated the effectiveness of the MDE-Grey model in detecting rumors and explaining the results.

Physical modeling of nucleosome clustering in euchromatin resulting from interactions between epigenetic reader proteins

Euchromatin is an accessible phase of genetic material containing genes that encode proteins with increased expression levels. The structure of euchromatin in vitro has been described as a 30-nm fiber formed from ordered nucleosome arrays. However, recent advances in microscopy have revealed an in vivo euchromatin architecture that is much more disordered, characterized by variable-length linker DNA and sporadic nucleosome clusters. In this work, we develop a theoretical model to elucidate factors contributing to the disordered in vivo architecture of euchromatin. We begin by developing a 1D model of nucleosome positioning that captures the interactions between bound epigenetic reader proteins to predict the distribution of DNA linker lengths between adjacent nucleosomes. We then use the predicted linker lengths to construct 3D chromatin configurations consistent with the physical properties of DNA within the nucleosome array, and we evaluate the distribution of nucleosome cluster sizes in those configurations. Our model reproduces experimental cluster-size distributions, which are dramatically influenced by the local pattern of epigenetic marks and the concentration of reader proteins. Based on our model, we attribute the disordered arrangement of euchromatin to the heterogeneous binding of reader proteins and subsequent short-range interactions between bound reader proteins on adjacent nucleosomes. By replicating experimental results with our physics-based model, we propose a mechanism for euchromatin organization in the nucleus that impacts gene regulation and the maintenance of epigenetic marks.

Flux dependence of redshift distribution and clustering of LOFAR radio sources

We study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale structure of the Universe, examining flux density limited samples (1 mJy, 2 mJy, 4 mJy and 8 mJy) of LoTSS wide field radio sources. We utilise and combine the posterior probability distributions of photometric redshift determinations for LoTSS deep field observations from three different fields (Boötes, Lockman hole and ELAIS-N1, together about $26$ square degrees of sky), which are available for between $91<!PCT!>$ to $96<!PCT!>$ of all sources above the studied flux density thresholds and observed in the area covered by multi-frequency data. We estimate uncertainties by a bootstrap method. We apply the inferred redshift distribution on the LoTSS wide area radio sources from the HETDEX field (LoTSS-DR1; about $424$ square degrees) and make use of the Limber approximation and a power-law model of three dimensional clustering to measure the clustering length, $r_0$, for various models of the evolution of clustering. We find that the redshift distributions from all three LoTSS deep fields agree within expected uncertainties. We show that the radio source population probed by LoTSS at flux densities above $1$ mJy has a median redshift of at least $0.9$. At $2$ mJy, we measure the clustering length of LoTSS radio sources to be $r_0 = (10.1 $Mpc in the context of the comoving clustering model. Our findings are in agreement with measurements at higher flux density thresholds at the same frequency and with measurements at higher frequencies in the context of the comoving clustering model. Based on the inferred flux density limited redshift distribution of LoTSS deep field radio sources, the full wide area LoTSS will eventually cover an effective (source weighted) comoving volume of about $10\ $ Gpc$^3$.

A Clustering Model for Three-Way Asymmetric Proximities: Unveiling Origins and Destinations

In many real-world situations, the available data consist of a set of several asymmetric pairwise proximity matrices that collect directed exchanges between pairs of objects measured or observed in a number of occasions (three-way data). To unveil patterns of exchange, a clustering model is proposed that accounts for the systematic differences across occasions. Specifically, the goal is to identify the groups of objects that are primarily origins or destinations of the directed exchanges, and, together, to measure the extent to which these clusters differ across occasions. The model is based on two clustering structures for the objects, which are linked one-to-one and common to all occasions. The first structure assumes a standard partition of the objects to fit the average amounts of the exchanges, while the second one fits the imbalances using an “incomplete” partition of the objects, allowing some to remain unassigned. In addition, to account for the heterogeneity of the occasions, the amounts and directions of exchange between clusters are modeled by occasion-specific weights. An Alternating Least-Squares algorithm is provided. Results from artificial data and a real application on international student mobility show the capability of the model to identify origin and/or destination clusters with common behavior across occasions.

New approach for learning structured graph with Laplacian rank constraint

Many existing graph-clustering methods get results in a two-stage manner, including constructing a graph from data and partitioning it. It always leads to sub-optimal performances. To address the drawbacks, imposing rank constraint on the graph’s Laplacian matrix is an effective way. It can learn a structured graph that connected components are exactly equal to the cluster numbers. According to separate connected components, it directly obtains clustering assignments. However, previous works heuristically solve it during the optimization without any theory guarantee. To handle this issue, we propose a novel solver to optimize this problem with theoretical advantages, enabling the structured bipartite graphs to directly indicate clustering results. Initially, we discover that pairwise and bipartite graphs exhibit the same connected components after undergoing a specific operation. Then, we derive an efficient algorithm to optimize two different clustering models with rank constraints based on this property. Moreover, our proposed method exhibits linear computational complexity to the data scale. Extensive empirical results on both synthetic and real benchmark data demonstrate that the proposed method outperforms existing methods.

Hybrid data clustering algorithm and interactive experience in E-learning electronic course simulation of legal education

With the development of multimedia technology, interactive entertainment experience is an important feature of online teaching mode. Students can engage in interactive learning through various mobile apps and online tools. E-learning provides an innovative way of learning that enables distance learning through electronic course simulation. The objective of this research is to design and develop an improved E-learning course simulation for law teaching by applying hybrid data clustering algorithms and enhancing interactive experiences to improve student engagement and learning outcomes. This article analyzes the simulation of legal education E-learning electronic courses based on mixed data clustering algorithms and interactive experiences. On the basis of the mixed data clustering model of the k-means algorithm, we propose a probability based mixed clustering model that can effectively handle the clustering problem of mixed data. Based on this algorithm, we have established a legal teaching network system platform based on artificial intelligence. The hybrid data clustering algorithm solves the instability problem of the k-means algorithm in the construction of the legal teaching network course platform, providing a reliable solution for the development of artificial intelligence network courses. Based on teaching concepts and combined with various technological characteristics such as network technology and multimedia technology in the information age, users who pass through this teaching system can fully utilize the teaching resources and various functions in the system. The hybrid data clustering algorithm proposed in this article has good clustering performance.

Reviewing Optimization Techniques in Supply Chains: AI and Blockchain Perspectives

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) into Supply Chain Management (SCM) has transformed the industry by enhancing efficiency, accuracy, and responsiveness. This review paper provides a comprehensive analysis of current AI applications in SCM, focusing on demand forecasting, inventory management, logistics, and transportation. Various AI techniques, including time-series forecasting, clustering, neural networks, SARIMA, and LSTM models, are discussed in detail. The impact of cutting-edge technologies on supply chain traceability and efficiency, such as blockchain and the Internet of Things (IoT), is also examined in this article. Despite the significant advancements, challenges such as gaps in closed-loop supply chains, terminology inconsistencies, and the need for better technical-managerial alignment persist. This review recognizes future research directions to address and solve these challenges and highlights the potential for AI to drive further innovations in SCM. Through case studies and bibliometric analyses, this paper underscores the significance of a comprehensive strategy for supply chain redesign, integrating physical, facilities, and information management to enhance sustainability and market responsiveness. Keywords— Supply chain management , Artificial Intelligence (AI), Big Data Analytics (BDA), Demand forecasting, Inventory management, Logistics optimization, Blockchain , Internet of Things (IoT), Smart Transportation, Tactical planning, Strategic planning, Advanced available-to-promise (AATP), Sustainability in supply chain.