Optimal Clustering Research Articles

Application of recommendation system based on fuzzy logic in music culture communication and music teaching management

Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.

AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm

Clustering, as an essential technique in unsupervised learning, plays a pivotal role in the fields of data mining and machine learning. However, the classic K-means clustering algorithm has intrinsic drawbacks such as sensitivity to initial cluster centers, susceptibility to a local optimal solution, and challenges in handling data uncertainty. To address these problems, this paper proposes an artificial hummingbird algorithm (AHA)-based three-way K-means clustering algorithm, called AHA-3WKM. First, AHA is introduced to address the problems of sensitivity to initial cluster centers and local optima. Second, a fitness function of AHA is specifically constructed to find the best initial clustering centers so that the hummingbirds can search for high-quality food sources, i.e., the global optimum cluster centers. Third, a three-way clustering approach is utilized to capture information about data uncertainty. In this way, the results of clustering are divided into three distinct regions based on the relationship between objects and clusters. The experimental results demonstrate that AHA-3WKM has good performance, and enhances the stability and the accuracy of clustering results.

Klasifikasi Penggunaan Teknologi Pada Petani Milenial di Sulawesi Selatan Menggunakan Density Based Spatial Clustering Algorithm With Noise

This research examines the use of agricultural technology by millennial farmers in South Sulawesi Province by applying the Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) to the agricultural census data of South Sulawesi Province Phase 1 in 2023. The secondary data used includes the number of millennial farmers aged 19-39 who use or do not use digital technology, divided by district/city and gender. The analysis process begins with preprocessing to prepare the data, followed by clustering using the DBSCAN algorithm, determining the optimal values for the Eps and minPts parameters, and evaluating the quality of the formed clusters using the silhouette coefficient and elbow method. The results of the study indicate that the combination of Eps value of 1.000 and minPts value of 7 produces optimal clustering with 2 clusters formed and 92 data points clustered, while 4 other data points are considered as noise. Evaluation using the silhouette coefficient and elbow method also indicates that the optimal data grouping is k=2.

DEA-based internal validity index for clustering

Internal validity indices are crucial in evaluating the quality of clustering results, serving as valuable tools for comparing various clustering algorithms and determining the optimal number of clusters for datasets. Most existing internal validity indices use the worst-case scenario to represent the overall validity. Moreover, some indices assign equal weights to distances among different clusters, even when these distances might have varying degrees of influence on the overall validity. Data envelopment analysis (DEA) is an effective technique for evaluating the performance of decision-making units through the computation of the ratio of the weighted sum of outputs to the weighted sum of inputs. The weight assigned to each indicator signifies its degree of influence on efficiency. Furthermore, DEA can be viewed as a multiple-criteria evaluation methodology, wherein inputs and outputs are two sets of performance criteria. We propose a DEA-based internal validity index (DEAI) to evaluate the validity of the clustering results. In this approach, intra-cluster compactness and inter-cluster separation are employed for determining the input(s) and output(s). The DEAI is then applied to the artificial datasets and empirical examples. Experimental results illustrate that DEAI outperforms six classic internal validity indices in accurately identifying the optimal cluster across all 10 datasets.

Industrial Big Data‐Driven Modeling and Prediction for Hot‐Rolled Strip Crown with Multigrade and Multispecification Data

In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot‐rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K‐Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot‐rolled strip crown in the future.

Safety Monitoring Method for the Uplift Pressure of Concrete Dams Based on Optimized Spatiotemporal Clustering and the Bayesian Panel Vector Autoregressive Model

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spatially cluster the uplift pressure measuring points at different locations on the dam; three distance indexes and two clustering evaluation indexes are used to realize clustering optimization and select the optimal clustering results. The Bayesian panel vector autoregressive model is used to establish the uplift stress safety monitoring model for each category of monitoring point. For a nonstationary sequence, the difference method is selected to ensure that the sequence is stable, and the prediction is carried out according to the presence or absence of exogenous variables. The result is that the addition of exogenous variables increases the accuracy of the model’s forecast. Engineering examples show that the uplift pressure measurement points on the dam are divided into seven categories, and classification is based mainly on location and influencing factors. The multiple correlation coefficients of the training set and test set data of the BPVAR model are more than 0.80, and the prediction error of the validation set is lower than that of the Back Propagation neural network, XGBoost algorithm, and Support Vector Machines. The research in this paper provides some reference for seepage monitoring of concrete dams.

A New Hybrid Optimization Algorithm to Solve the Clustering Problem

: This paper presents a novel hybrid technique, merging the K-means algorithm with Genetic Algorithm (GA), aiming to enhance clustering performance. This approach leverages the strengths of both algorithms, enabling improved cluster generation by overcoming individual algorithmic limitations. The GA-KM algorithm is introduced to aid K-means in avoiding local optima, with GA exhibiting proficiency in determining optimal cluster initialization and parameter optimization. The focus is on developing a GA-based algorithm for generating high-quality clusters efficiently. Notably, the research explores the application of this hybrid approach to address issues in the educational domain, specifically for out-of-school children. The fitness function in GA is tailored to the problem area, emphasizing the need for an appropriate system to study and address school children's problems. The research proposes a hybrid algorithm (KM-GA-NM-PSO) that amalgamates the best features of existing algorithms, thereby overcoming individual limitations and promising superior results. This hybridization is expected to yield high-quality clusters with minimal function evaluations, outperforming other methods by producing clusters with small standard deviations on selected datasets. The proposed approach, combining KM, GA, NM, and PSO algorithms, demonstrates improved data clustering quality and algorithmic efficiency.

Study on cluster centers optimization of max-min distance k-means clustering algorithm based on inter-cluster separation measure

Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability.

Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN

WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Inappropriate YouTube content detection and classification by using proposed novel auto-determined k-means clustering and PDBRNN architecture

To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual pre-processing; however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9%.

Online English Resource Integration Algorithm based on high-dimensional Mixed Attribute Data Mining

To improve the scalability of resources and ensure the effective sharing and utilization of online English resources, an online English resource integration algorithm based on high-dimensional mixed-attribute data mining is proposed. First, an integration structure based on high-dimensional mixed-attribute data mining is constructed. According to this structure, the characteristics of online English resources are extracted, and historical data mining is carried out in combination with the spatial distribution characteristics of resources. In this way, the spatial mapping function of features is established, and the optimal clustering center is designed according to the clustering and fusion structure of online English resources. At this node, the clustering and fusion of online English resources are carried out. According to the fusion results, the distribution structure model of online English resources is constructed, and the optimization research of the integration algorithm of online English resources is carried out. The experimental results show that the integration optimization efficiency of the proposed algorithm is 89%, and the packet loss rate is 0.19%. It has good integration performance, and can realize the integration of multi-channel and various forms of online English resources.

A preliminary study unveils CISD2 as a ferroptosis-related therapeutic target for recurrent spontaneous abortion through immunological analysis and two-sample mendelian randomization

Recurrent spontaneous abortion (RSA) affects approximately 1 % of women striving for conception, posing a significant clinical challenge. This study aimed to identify a prognostic signature in RSA and elucidate its molecular mechanisms. Prognostic gene impacts were further assessed in HTR-8/SVneo and human primary extravillous trophoblast (EVT) cells in vitro experiments. A total of 6168 differentially expressed genes (DEGs) were identified, including 3035 upregulated and 3133 downregulated genes. WGCNA pinpointed 8 significant modules and 31 ferroptosis-related DEGs in RSA. Optimal clustering classified RSA patients into three distinct subgroups, showing notable differences in immune cell composition. Six feature genes (AEBP2, CISD2, PML, RGS4, SRSF9, STK11) were identified. The diagnostic model showed high predictive capabilities (AUC: 0.966). Mendelian randomization indicated a significant association between CISD2 levels and RSA (OR: 1.069, P-value: 0.049). Furthermore, the downregulation of CISD2 promotes ferroptosis in HTR-8/SVneo and human primary EVT cells. CISD2 emerged as a pivotal gene in RSA, serving as a ferroptosis-related therapeutic target. The diagnostic model based on gene expression and Mendelian randomization provides novel insights into the pathogenesis of RSA.

A novel batch-selection strategy for parallel global optimization

ABSTRACT Bayesian-based parallel global optimization methods have become increasingly popular for solving black-box functions using parallel resources. When the function exhibits multimodality, it is difficult to identify the global optimum. This article proposes a novel batch selection strategy for parallel global optimization called Sampling–Optimizing–Selecting (SOS) to address this problem. SOS, utilizing minimum energy design, gradient optimization and hypersphere clustering, can adaptively explore multiple local optimal regions of the objective function simultaneously and make full use of parallel resources to obtain the global optimum efficiently. Additionally, the SOS algorithm maintains the sparsity between sample points to a certain extent, which enhances the accuracy of the surrogate model and thus improves the robustness of the optimization effect. Several numerical simulations and optimization experiments involving array decoy jamming are presented to demonstrate the superiority of the proposed strategy.

Novel Hybrid Deep Optimized Machine Learning Model for Energy Efficient Cluster Selection in Unmanned Aerial Vehicle Networks

Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches.

Hybrid metaheuristic schemes with different configurations and feedback mechanisms for optimal clustering applications

This paper addresses the critical gap in the understanding of the effects of various configurations and feedback mechanisms on the performance of hybrid metaheuristics (HMs) in unsupervised clustering applications. Despite the widespread use of HMs due to their ability to leverage multiple optimization methods, the lack of comprehensive studies on their configuration and feedback mechanisms effects often results in sub-optimal clustering performances and premature convergence. To tackle these issues, we introduce two algorithms for implementing eight distinct HM schemes, focusing on the impacts of parallel and serial processing models along with different feedback mechanisms. Our approach involves selecting candidate metaheuristics based on a mix of evolutionary and swarm-based methods, including the k-means algorithm, to form various HM-based clustering schemes. These schemes were then rigorously evaluated across a range of datasets and feedback mechanisms, further assessing their efficiency in the deployment of smart grid base stations. Performance analysis was based on total fitness evaluations, timing capabilities, and clustering accuracy. The results revealed that parallel HMs with decoupled feedback mechanisms performed best in terms of accuracy but at the cost of slower convergence rates as compared to serial HMs. Our findings further suggest that serial HMs will be best suited for time-sensitive applications where a compromise between speed and accuracy is acceptable, while parallel HMs with decoupled feedback mechanisms are preferable for scenarios where precision is paramount. This research significantly contributes to the field by providing a detailed analysis of HM performance in varying conditions, thereby guiding the selection of appropriate HM schemes for specific clustering tasks.

Football Player Classification

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms.

Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model

Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.

Eligibility Study of Targeted Electricity Subsidies Using DBSCAN on 450 VA and 900 VA Households at PLN UP3 Bandung

PT PLN (Persero), a State-Owned Enterprise (SOE), is mandated by Law No. 30/2007 on Energy and Law No. 30/2009 on Electricity to provide subsidy funds for the poor. The objective of this study is to analyze eligibility criteria for electricity subsidy recipients for customers using 450 VA and 900 VA power groups, to target the electricity subsidy program better. The data used is postpaid customer data from UP3 Bandung in September 2023. The variables used are the amount of electricity consumption, the number of bills, late fees, installment fees, and 50 other variables. The method used in this research is DBScan Clustering which is applied to each power group. Within each group, we analyzed two normalized versions of the dataset standard version and the minmax version. Furthermore, to assess the optimal clustering results, we integrated various metrics, including the Davies-Bouldin Index and Silhouette Score with visual assessment. After that, the best factor suggestions were sought through decision trees, by performing different decision tree classifiers for each power group, using normalized versions of cluster labels. The results showed that among the 50 features available in the raw dataset, it was successful in identifying key features, such as late fees, installment fees, electricity consumption, and bill charges to be important criteria

Tailored clustering method to identify quasi-regional sites

Classical clustering (CC) normally divides a database into a predetermined number of fixed clusters before assigning each target site into one of these clusters to build a quasi-local model. Because the range of target sites is much larger than the range of clusters, it is possible that the identified cluster contains database sites that are weakly correlated to some assigned target sites, especially when these target sites are located near the cluster boundaries. The existence of weakly correlated sites in the cluster may make the quasi-local model less effective in predicting soil properties at the target site. This paper proposes a Tailored Clustering Enabled Regionalization (TCER) framework that can minimize the inference uncertainty at a target site. It mainly consists of two parts: (1) a site similarity measure to quantify the similarity between database sites and the target site and (2) a novel tailored clustering (TC) approach to identify the optimal cluster (called quasi-regional cluster) of database sites with the highest site similarity measures. In comparison to the traditional TC, the novel TC does not require users to specify a site similarity threshold that divides database sites into those within the cluster and those outside the cluster. Finally, a hierarchical Bayesian model is applied to the quasi-regional cluster (rather than the entire database) to infer geotechnical/geological properties at the target site. The capability of TCER is verified using synthetic and real examples. It is shown that the proposed TCER is computationally efficient and can reduce inference uncertainty.