Good Clustering Effect Research Articles

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Large-scale multi-view spectral clustering based on two-stage well-distributed anchor selection

Spectral clustering has attracted much attention because of its good clustering effect, but its high computational cost makes it difficult to apply to large-scale multi-view clustering. In response to this issue, a simple and efficient large-scale multi-view spectral clustering algorithm is proposed, which is based on a Two-stage Well-distributed Anchor Selection strategy (TWAS). Firstly, the data set is divided into several disjoint sample blocks to get the global well-distributed anchor candidate. Then, the algorithm proceeds to select anchor points within each local candidate anchor set. This two-stage anchor selection strategy facilitates the identification of anchors with significant representativeness at a reduced computational expense, thereby adeptly capturing the intrinsic data structure. Secondly, the present study devises an adaptive near-neighbor graph learning approach to construct an anchor-based intra-view similarity matrix. Finally, the multiple views are fused to obtain a consistent inter-view similarity matrix, and the clustering results are obtained. Extensive experiments demonstrate the effectiveness, efficiency, and stability of the TWAS algorithm.

Nutritional and Scientific Research on Chinese Culinary Foods Based on Big Data Analysis

Abstract The current Chinese cooking nutritional and scientific degree is not high. Chinese cooking technology has made it difficult to meet people’s health needs. This paper explores the path of nutritional scientificization in digital cooking. The FCM algorithm based on MapReduce has been found to have a good clustering effect and execution efficiency. Based on big data processing for fuzzy clustering application research, the samples are preprocessed using big data processing techniques to determine the functions in the sample descriptions. The data analysis is completed using Mapreduce-based parallelized FCM, and the rules for optimizing the objective function are given. The method proposed in this paper can efficiently and accurately complete the data analysis, as evidenced by examples. Residents can choose the cooking technique that best suits their nutritional needs by reducing the ABT antioxidant capacity and FRAP antioxidant capacity of coriander by 42.67% and 44.21% by boiling and high pressure. Big data analysis-based cooking techniques outperformed traditional cooking techniques in all dimensions, ensuring a balance between food flavor and health and wellness.

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Abstract This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages of low cost and sustainability. Bicycle-sharing operators use electronic fence parking points to manage bicycle-sharing, but it is too time-consuming and impractical to manage them individually. Therefore, it is necessary to cluster the electronic fence parking points and implement regionalized management. This study uses a Mean-shift clustering algorithm to cluster the electronic fence parking points on Xiamen Island, construct a bicycle-sharing dispatching station, and divide the management area. Singular value decomposition is employed to analyze the travel demand patterns of bicycle-sharing and travel characteristics. In addition, we constructed regression models to explore the relationship between the urban built environment and bicycle-sharing trips during the morning and evening peak hours. The study results show that: (1) The 73 dispatching stations constructed cover 86.72% of the bicycle-sharing data, with a good clustering effect. (2) The travel demand for bicycle-sharing shows multiple patterns and different characteristics in different spatial and temporal distributions, which are influenced by land use. (3) There are spatial and temporal differences in the impact of the urban built environment on bicycle-sharing trips, especially residential and enterprise poi densities have opposite effects on shared bicycle-sharing during morning and evening peak hours. The research results of this paper can serve in the planning of bicycle-sharing dispatch stations and the differentiated management and dispatch of bicycle-sharing, which can reduce operating costs and promote the development of sustainable urban transport.

Deep Mining Algorithm of Online Purchase Behavior Data Based on Decision Tree Model

Abstract It is difficult to mine online buying behavior data by ignoring the classification of online buying behavior data, and the precision and recall are both on the low side. The training set of online buying behavior data is processed by top-down recursion, and a single decision tree is created recursively, and a decision tree classification model is constructed. Based on the classification results of behavior data, the regular estimation of online shopping features is calculated by preprocessing customer behavior features, and the deep mining algorithm is designed. Experimental results show that the decision tree model has good data clustering effect. Based on this, the precision and recall of online shopping behavior data mining are high, and the application performance is ideal.

Automatic Identification Method of HPLC Platform Topology Based on Characteristic Data Extraction

The topological structure of distribution network system is complex, and the operation state changes frequently, so the obtained distribution network topological information has a high error. Therefore, this article proposes a clustering feature extraction method of load curve based on singular value decomposition. The load curve is given by the invariance of singular vectors to improve the generalization ability of feature processing. On the basis of considering the weight of load feature, the data integrity is ensured by singular value curve, and the clustering accuracy is high, which makes the load feature have practical physical significance. The experimental results show that this method can achieve good clustering effect, reduce the clustering time, improve the reliability of data transmission and communication coverage, and meet the communication access requirements of the power Internet of Things sensing layer.

Ship Trajectory Clustering Based on Trajectory Resampling and Enhanced BIRCH Algorithm

Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a ship trajectory clustering method based on ship trajectory resampling and enhanced BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed. The method has been tested using 764,393 AIS trajectory points of 13,845 ships in the waters of the Taiwan Strait of China, and 832 ship trajectories have been generated and clustered to obtain 172 classes of ship trajectory line clusters among 40 port pairs. The experimental results show that the proposed method has exhibited a good clustering effect on ship trajectories. Compared with the existing ship trajectory clustering methods, the proposed method can more efficiently detect and identify differences between trajectories with largely similar spatial distribution characteristics, so as to obtain legitimate clustering results. In addition, this study has constructed the main ship navigation routes between ports based on the extracted ship trajectory line clusters, and the constructed main routes are directional, refined, and rich in content compared with the existing ship routes. This research provides theoretical and technical support for ship route planning and maritime traffic management.

Clustering method of unbalanced large data density based on dynamic grid

In order to effectively ensure the clustering quality of unbalanced big data density, improve the clustering accuracy of unbalanced big data density and shorten the clustering time of unbalanced big data density, an unbalanced big data density clustering method based on dynamic grid is proposed. This paper analyzes the definition and characteristics of dynamic grid, and expounds the clustering method based on density and dynamic grid. Build a dynamic grid of unbalanced big data through unbalanced big data database. Consider a single spatial object, divide the unbalanced big data dynamic grid, and calculate the cell density of the unbalanced big data dynamic grid. Dynamic grid technology is adopted to realize unbalanced large data density clustering. Experimental results show that the proposed algorithm has good clustering effect, which can effectively improve the clustering accuracy of unbalanced big data and shorten the clustering time of unbalanced big data density.

An Automatic Identification Method of Crested Ibis (Nipponia nippon) Habitat Based on Spatiotemporal Density Detection.

Simple SummaryThe trajectory data of crested ibis (Nipponia nippon Temminck, 1835) have been obtained by HQBG3621L backpack-style tracker. By combining the spatial and temporal features of the trajectory data, an improved spatiotemporal clustering-based DBSCAN method was adopted to detect crested ibis’s stopping points and identify the crested ibis’s habitat. The clustering results are consistent with those from remote sensing images and field surveys.To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nippon Temminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.

Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

In order to break through the limitations of current clustering algorithms and avoid the direct impact of disturbance on the clustering effect of abnormal big data texts, a big data text clustering algorithm based on swarm intelligence is proposed. According to the characteristics of swarm intelligence, a differential privacy protection model is constructed; At the same time, the data location information operation is carried out to complete the location information preprocessing. Based on KD tree division, the allocation of the differential privacy budget is completed, and the location information is clustered by dimension reduction. Based on the result of the location information dimension reduction clustering, a differential privacy clustering algorithm is proposed to maximize the differentiation of the clustering effect. Select the fuzzy confidence and support thresholds to extract the associated features, use the features to determine the big data text targets to be clustered, establish a target decision matrix, determine the clustering targets, and obtain the target weights, the obtained membership, and the decision results. Realize the effective clustering of big data text based on swarm intelligence. The experimental results show that the algorithm can effectively realize the high-efficiency clustering of big data texts, and the clustering time is short, and it has a good clustering effect.

Lipid metabolomic analysis in exosomes of osteonecrosis of the femoral head based on ultra performance liquid chromatography-tandem mass spectrometry

股骨头坏死(ONFH)是一种可导致股骨头塌陷进而需要接受全髋关节置换的疾病。外泌体作为一种细胞间交流的方式,在一系列生理和病理过程中起着至关重要的作用,已在疾病的诊断和治疗中发挥独特作用。该研究利用非靶向代谢组学方法,探讨股骨头坏死组织外泌体内的脂质代谢特征,阐释股骨头坏死时机体发生的脂质代谢变化。该研究采用超速离心的方法,对股骨头坏死组织的外泌体进行了分离富集,并使用动态光散射(DLS)、蛋白质免疫印迹和透射电子显微镜(TEM)3种方法鉴定外泌体。采用超高效液相色谱-串联质谱(UPLC-MS/MS)结合多变量统计分析识别股骨头坏死外泌体的脂质代谢谱。采用主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA)对差异表达的外泌体脂质代谢物进行多变量统计分析。在外泌体中检测到18种明显改变的脂质代谢物,包括丙烯醇酯类、脂肪酸酯类、甘油酯类及其衍生物。通过代谢分析网站进行通路分析,从而确定受影响的代谢通路并进行可视化。代谢通路分析显示外泌体内的甘油磷脂代谢和鞘脂代谢改变最为明显,鞘脂和甘油磷脂之间的不平衡导致脂肪毒性损伤,这与常见代谢性疾病的病理生理学有关。同时,甘油磷脂与细胞增殖、分化和凋亡之间具有相关性,甘油磷脂比例的变化可以反映脂质代谢的紊乱。外泌体内的脂质代谢变化可能在一定程度上反映了ONFH疾病的代谢变化。ONFH外泌体脂质代谢组学分析可有助于探索坏死骨组织外泌体中的脂质代谢变化和受影响的脂质代谢通路。

A self-adaptive DBSCAN-based method for wafer bin map defect pattern classification

The wafer map is obtained by testing each die in the wafer during semiconductor production for defects and marking the defective die. The classification of wafer maps can provide evidence for problems occurring in the production process, so as to solve the problems and reduce the costs. Before classifying the wafer map, the most important thing is feature extraction. In addition to a certain spatial pattern, the wafer map also has a lot of noise, which affects the process of feature extraction. When the traditional DBSCAN algorithm is used for filtering, it is necessary to manually determine the value of Eps and MinPts parameters, and the selection of the parameters directly affects the accuracy of the clustering. Therefore, this paper proposes an automatic parameter filtering method based on DBSCAN, which can solve the traditional drawbacks of manually parameters setting, the algorithm is a Self-Adaptive DBSCAN-based method for wafer bin map, we call it SA-DBSCANWBM. This method selects a comprehensive index of cluster intra-cluster density and inter-cluster density to evaluate the optimal parameters. The experimental results show that the algorithm proposed in this paper can automatically and reasonably select better parameters and has a good clustering effect, which helpful for subsequent feature extraction and classification.

Density peaks clustering based on geodetic distance and dynamic neighbourhood

Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilising this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighbourhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbours K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other five clustering algorithms on six synthetic datasets and ten real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets.

An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection‐based SCA‐SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection‐based SCA‐SNN algorithm. Furthermore, compared to the other state‐of‐the‐art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

Spatial clustering analysis of green economy based on knowledge graph

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm.

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

Density peaks clustering based on geodetic distance and dynamic neighbourhood

Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilisin...

Research on Seafood Traceable Data Based on k-Modes Clustering Algorithm

Ge, L.; Li, J., and Chen, J., 2020. Research on seafood traceable data based on k-modes clustering algorithm. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 73–77. Coconut Creek (Florida), ISSN 0749-0208.The improved k-modes clustering algorithm for classification attribute data is proposed, in which the classification attribute data of seafood traceable data are taken as an example and the k-modes algorithm is improved by improving clustering accuracy and process. The improved k-modes clustering algorithm combines density and distance to select the initial center point of cluster, which ensures the effectiveness of the initial cluster center and avoids falling into local extreme points so that clustering process can be simplified. The improved k-modes clustering algorithm redefines the clustering mode and comprehensively considers the representative of all attribute values in sample attributes to clustering category so that the distance measure will be improved and that the clustering effect can be optimized. Experiments show that the improved k-modes clustering method has good clustering effect on standard data sets, which is universal to classification attribute data and worthy of being more popular and further improving in practice.