Selection Of Initial Cluster Centers Research Articles

Research on Gas Multi-indicator Warning Method of Coal Mine Working Face Based on MOA-Transformer.

The gas emission from the coal mine working face is influenced by multiple factors, resulting in the real-time value, fluctuation, and trend changes of gas concentration being relatively independent and interrelated. This paper establishes a gas multi-indicator warning method that can comprehensively warn the status of real-time value, fluctuation, and trend changes of gas concentration from the working face. This paper proposes six basic indicators and includes two main research contents: intelligent threshold partition and gas multi-indicator warning. First, this paper proposes an intelligent threshold partition algorithm based on GF-KMeans (genetic fixed-centered K-means), which combines a genetic algorithm (GA) and an FC-KMeans (fixed-centered K-means) algorithm to dynamically partition the threshold range corresponding to the gas warning level. The GA solves the local optimal problem in the traditional K-Means algorithm, enhancing its stability and predictability. The FC-KMeans algorithm achieves a more precise control in the initial clustering center selection. Second, this paper researches a gas multi-indicator warning method based on a multihead optimal attention (MOA)-Transformer. By using the multihead optimization attention mechanism to represent classification features and utilizing Transformer's encoder structure to classify gas warning. The experimental result shows that the accuracy of the MOA-Transformer method is 86.17%, which is 3.45% higher than that of the Transformer method. The precision of the MOA-Transformer method is 88.78%, which is 3.75% higher than that of the Transformer method. The recall of the MOA-Transformer method is 85.23%, which is 4.70% higher than that of the Transformer method. The macro-F1 of the MOA-Transformer method is 86.96%, which is 4.39% higher than that of the Transformer method. The results fully demonstrate the superiority of the MOA-Transformer method in gas warning tasks.

A novel tree structure-based multi-prototype clustering algorithm

Density-peak clustering (DPC) is a novel clustering algorithm that has received much attention from researchers. Its principle is to identify the cluster center of the data by density and distance. However, the density-peak clustering algorithm still suffers from poor clustering results due to irrational choices of cluster centers and misallocating samples. To tackle these problems, a novel tree structure-based multi-prototype clustering algorithm (MPCTS) is proposed in this paper. Firstly, a division-merger method is adopted to divide the dataset into multiple peak clusters based on multi-prototype clustering, which could minimize the impact of high-density sample points on the subsequent sample point assignments. Secondly, the KNN nearest-neighbor search strategy is improved to quickly obtain the sub-cluster center in each peak cluster to overcome the defect of irrational selection of initial cluster centers. Afterward, a tree-based decay strategy is designed to evaluate the correlation between sample points, and a tree-based association strategy is designed to achieve the merging of multiple peak clusters to tackle the sample points misallocation problem. Finally, the extensive experiments on clustering task are conducted over several benchmark synthetic and real datasets to verify the efficiency and superiority of the proposed MPCTS.

Discussion on the use of traditional art colors in modern visual communication design

Abstract In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot spot of attention. In this paper, a model for extracting traditional art color features is constructed to provide a methodology and theory for applying traditional art color in modern visual communication design. In the model construction, the max-min clustering algorithm is proposed to achieve color segmentation, measure the distance between the clustering center and the samples, and improve computing speed. Based on the K-means algorithm, the initial clustering center selection and the number of class clusters are improved, and the color feature extraction method is optimized to improve the accuracy of color extraction. In the performance experiments of the model, the All, Noncc, and Disc parameters of this paper’s color segmentation algorithm are lower than those of other algorithms in color segmentation of different pictures, and the performance is good. The accuracy of the color feature extraction test in different color spaces is close to 100%, the average verification accuracy is as high as 99.8%, and it takes less time. The aesthetic, originality, symbolism, and spirituality evaluation indexes of the poster work “Lead,” designed by combining the model of this paper, have an average score of more than 4, which reaches a good level.

Credit rating algorithm of corporate bonds based on Gaussian process mixture model and improved K-means

The primary challenge in credit analysis revolves around uncovering the correlation between repayment terms and yield to maturity, constituting the interest rate term structure-an essential model for corporate credit term evaluation. Presently, interest rate term structures are predominantly examined through economic theoretical models and quantitative models. However, predicting treasury bond yields remains a challenging task for both approaches. Leveraging the clustering analysis algorithm theory and the attributes of an insurance company’s customer database, this paper enhances the K-means clustering algorithm, specifically addressing the selection of initial cluster centers in extensive sample environments. Utilizing the robust data fitting and analytical capabilities of the Gaussian process mixture model, the study applies this methodology to model and forecast Treasury yields. Additionally, the research incorporates customer credit data from a property insurance company to investigate the application of clustering algorithms in the analysis of insurance customer credit.

An analysis of the innovation strategy of student management in universities based on big data

Abstract This paper collates and fuses data from various aspects of university students and designs student evaluation indexes by combining the data. Based on the student behavior segmentation model of cluster analysis, the traditional k-means clustering algorithm is optimized, focusing on the optimization of content selection of initial cluster centers and distance optimization calculation to make a better clustering effect and classify students accurately. Then the K-nearest neighbor algorithm is proposed to solve the problem of timeliness and posteriority of student warning, by which the behavior of students can be predicted, and teachers can manage and help students in time. Finally, a student behavior characteristic category model is established to strengthen the prediction model of the K-nearest neighbor algorithm and improve the accuracy of predicting student behavior. The student data of a university were analyzed, in which the correlation pearson coefficient between the number of library admissions and grades ranged from 0.25-0.31, and the p-values were all much less than 1. It showed that the number of library admissions of students obviously affected students’ grades. In the analysis of the correlation between the early rising index and academic achievement, where the Pearson coefficient is 0.291 with a p-value of 5.61E-7. This indicator, which is not much associated with academic behavior, indicates that students’ early rising and timely breakfast can affect achievement to some extent. Therefore, the method of this paper can be refined using student data.

A new Chinese text clustering algorithm based on WRD and improved K-means

Text clustering has been widely used in data mining, document management, search engines, and other fields. The K-means algorithm is a representative algorithm of text clustering. However, traditional K-means algorithm often uses Euclidean distance or cosine distance to measure the similarity between texts, which is not effective in face of high-dimensional data and cannot retain enough semantic information. In response to the above problems, we combine word rotator’s distance with the K-means algorithm, and propose the WRDK-means algorithm, which use word rotator’s distance to calculate the similarity between texts and preserve more text features. Furthermore, we define a new cluster center initialization method that improves cluster instability during random initial cluster center selection. And, to solve the problem of inconsistent length between texts, we propose a new iterative approximation method of cluster centers. We selected three suitable datasets and five evaluation indicators to verify the feasibility of the proposed algorithm. Among them, the RI value of our algorithm exceeds 90%. And for Marco_F1, our scheme was about 37.77%, 23.2%, 13.06% and 20.12% better than other four methods, respectively.

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative to limited geological sampling. However, the accuracy of sediment classification is limited due to constraints in acoustic data detection methods, data quality, and classification techniques. To address this issue, this study proposes an automatic classification method for marine sediment using an improved U-convolutional neural network and K-means clustering algorithm. In the coding part, a spatial pyramid pool layer is introduced to fuse low-dimensional feature data of different scales with the features of each level of the corresponding coding layer. This fusion method enhances the accuracy of the constructed relationship between the physical property parameters of the seabed bottom. The K-means clustering algorithm is optimized through selecting the point at the density center as the initial clustering center during the initial clustering center selection stage. This approach solves the sensitivity problem of the initial clustering center of K-means, improves the edge extraction effect of sediment types, and enhances the classification accuracy of sediment types. To validate the proposed method, an application test is conducted in the Northern Slope area of the South China Sea. The mean grain size of sediments in the study area is predicted using the improved U-Net neural network and the seafloor reflection intensity of the sub-bottom profile. Compared to the standard U-Net network results, the mean grain size prediction results show an increase of 4.9% and 2.8%, respectively. The sediment with the predicted mean grain size is then classified using the K-means clustering algorithm, resulting in the division of five sediment types: gravelly sand, sand, silty sand, sandy silt, and clayey silt. These classifications align well with the South China Sea sediment type map. The findings of this study not only provide an important supplement to existing marine sediment classification methods but also contribute significantly to understanding the sedimentary environment and processes in the Northern Slope of the South China Sea.

Research on the digital transformation path of banks in the era of big data

Abstract This paper proposes a weighted large-scale data subspace clustering algorithm to enable it to adapt to the mega-customer environment for financial banks to respond quickly to customer data. Firstly, based on the K-means combined with a genetic algorithm, an improved method for the sensitivity problem of initial clustering center selection of K-means algorithm is proposed. By weighting the variables and streaming data batch processing method as a guide, the improvement method is proposed for the problem that the mean algorithm cannot identify the correct clustering center caused by the ultra-large-scale data environment, leading to the iteration number approaching infinity. The accuracy of the K-mean algorithm, the optimized initial clustering center algorithm, and the algorithm in this paper are 89.61%, 94.37% and 96.94%, respectively. In terms of running time, the highest running time of this algorithm is 10.96 seconds, which is faster than the running time of the other two algorithms. Finally, the financial analysis of the financial bank that completed the digital transformation with the help of the algorithm in this paper, the bank achieved a business of 150.832 billion yuan in 2021, an increase of 11% compared with the end of last year. Net profit achieved 44.883 billion yuan, an increase of 25.8% compared to the end of last year. Therefore, the algorithm in this paper has high advantages in terms of accuracy, efficiency, and practicality, proving that digital transformation can improve bank profits. It also provides a path and direction of transformation for various urban and agricultural commercial banks and other small credit unions.

Centroid Based Celestial Clustering Algorithm: A Novel Unsupervised Learning Method for Haemogram Data Clustering

Accuracy of clustering is the most important parameter as far as automated disease identification is concerned. There have always been attempts to automate the process of disease prediction from haemogram data. However, there are several components in blood test results and very often we find that a variety of combinations of these component results are to be used to detect a disease. This makes identification of diseases really hard and necessitates the use of data analysis techniques. As new diseases are arising from time to time, a useful method for prediction is unsupervised learning and the corresponding data analysis technique is clustering. An easy, efficient and centroid based clustering algorithm that has been in practice widely is k-means. Its simplicity and efficiency make it a natural choice for most of the clustering applications. However, k-means is largely dependent on the selection of initial cluster centers and a bad choice can make it fall to local optima, thereby sacrificing accuracy. Besides, it is non-deterministic in nature. This paper proposes a novel, nature inspired, clustering method, named Centroid Based Celestial Clustering, which overcomes the above issues. Our method is deterministic and converges to global optima on spherical datasets. We experimentally evaluate our algorithm for speed of execution and cluster quality against well-known clustering algorithms using statistical evaluation metrics like silhouette width, adjusted rand index and Dunn index. We use the method to predict diseases identifiable from blood tests and our experiments show that the accuracy of prediction is very promising.

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Accurately clustering large, high dimensional datasets is a challenging problem in unsupervised learning. K-means is considered to be a fast, widely used and accurate centroid based data partitioning algorithm for spherical datasets. However, its non-determinism and heavy dependence on the selection of initial cluster centers along with vulnerability to noise make it a poor candidate for clustering large datasets with high dimensionality. To overcome these, we develop a novel, nature inspired, centroid based clustering algorithm, inspired from the principles of particle physics. Our method ensures that the convergence to local optima and non-deterministic outputs are avoided. We experiment the method on large datasets of human face images. Besides, our method addresses the problem of outliers and presence of not well-separated data in these datasets. We use a deep learning model for extracting facial features into a vector of 128 dimensions. We validate the quality and accuracy of our methods using different statistical parameters like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-measure, accuracy, error rate, average in group proportion and normalized cluster size rand index</i> . These evaluations show that our method exhibits better accuracy and quality in clustering large face image datasets, in comparison with other existing mechanisms. The strength of our algorithms is more visible as the size of the dataset grows.

DPCF: A framework for imputing missing values and clustering data in drug discovery process

The advent of modern Internet of Things (IoT) architectures has led to ease in data collection and availability. The data generated from such architectures are of large volume and dimensionality. As a result, data missingness and data labeling are the commonly occurring problems in the data collection process when data volume is too large. Data clustering is a commonly used unsupervised pattern classification technique that helps in identifying the hidden structure of the datasets and clusters or groups similar data items together. In context to chemometrics, clustering techniques play a significant role in identifying the structure-property relationships and structure-activity relationships among different compounds in drug discovery process. The quantitative structure-property relationships (QSPR) are based on the similar property principle which states that if the compounds are clustered together based on structural descriptors, the compounds within the same cluster will have similar properties. The quantitative structure-activity relationships (QSAR) help in determining the empirical relationships between chemical structure and biological activity of a set of identical compounds. Hence, a variety of compounds can be divided into homogeneous subsets using cluster analysis. The effectiveness of any clustering algorithm mostly depends on how efficiently the initial cluster centers are identified. Traditional techniques that mostly rely on the random selection of initial cluster centers and different parameter settings may result in distinct clusters for the same dataset. Moreover, if the initial cluster centers selected are outliers then the clusters formed are of poor quality. The quality of each cluster can be determined by using distinct cluster validation indices (CVIs). This paper aims to solve the problems of data missingness as well as data labeling for unsupervised datasets. In continuation to the previous study where the NMVI (Nullify the Missing Values before Imputation) technique efficiently imputes the missing values in different datasets, in this paper the proposed DPCF (Data Partitioning-based Clustering Framework) framework utilize the NMVI technique to impute the missing values and a novel Z-Clust clustering algorithm is proposed that efficiently clusters the unlabeled data samples. The integration of the NMVI technique and Z-Clust clustering technique in the proposed framework makes it well suited for the analysis of unsupervised datasets having missing values. The performance evaluation of the proposed Z-Clust clustering technique is done by using five standard CVIs and the results are compared with the existing clustering techniques. The experimental results depict that the proposed Z-Clust clustering technique shows better cluster formation as compared to the existing clustering techniques. Henceforth, the proposed DPCF framework is well suited for the analysis of datasets without labels and having missing values.

DP-Kmeans and Beyond: Optimal Clustering with a new Clustering Validity Index

&lt;p&gt;The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by dropping the random selection of the initial clustering centers and continuous updating of the new centers. Due to the unsupervised learning feature, the number of clusters and the quality of data partitions generated by the clustering algorithm cannot be guaranteed. In order to evaluate the results of the DP-Kmeans algorithm, this paper proposes the SII, a new clustering validity index based on the Sum of the Inner-cluster compactness and the Inter-cluster separateness. Based on the DP-Kmeans algorithm and the SII index, a new method is proposed to determine the optimal clustering numbers for different datasets. Experimental results on ten datasets with different distributions demonstrate that the proposed clustering method is more effective the existing ones. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

IMPLEMENTATION OF DDOS ATTACKS USING K-MEANS CLUSTERING BASED ON SEMISUPERVISED ALGORITHMS

Distributed denial of service (DDoS) attack is an attempt to make an online service unavailable by overwhelming it with traffic from multiple sources. Presents a semi-supervised weighted k-means detection method. Hybrid feature selection algorithm to find the most effective feature sets and propose an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal.

Design and Implementation of an Improved K-Means Clustering Algorithm

Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the average distance of the samples as the initial clustering center of the data set. Finally, combined with the “triangular inequality principle,” the unnecessary distance calculation of the KMeans algorithm in the iterative process is reduced, and the algorithm operation efficiency is improved. The results show that the improved KMeans clustering algorithm is tested on the UCI data set. Compared with the traditional k-means algorithm and Canopy-KMeans algorithm, the accuracy and speedup ratio of the improved KMeans clustering algorithm are significantly improved, and the clustering quality is improved.

A multi-stage heuristic algorithm based on task grouping for vehicle routing problem with energy constraint in disasters

In recent years, serious disasters have happened frequently, causing significant loss of lives and substantial economic consequences. Considering that rescue vehicles are mainly powered by electricity, they cannot compete with the conventional ones in terms of the cruising range. Therefore, it is of great significance to study how rescue vehicles can execute multiple rescue tasks quickly and efficiently with energy constraint. In this paper, a multi-stage vehicle routing algorithm based on task grouping (MSVR-TG) is proposed for the rescue vehicle routing problem with energy constraint (VRPEC) in disasters. In the task grouping stage, a novel K-means algorithm based on angular density (K-means-ad) is suggested to address the instability of the result of K-means caused by the random selection of initial cluster centers. It can balance the travel distance of each vehicle as much as possible, which is critical when vehicles are energy constrained. In the sequence planning stage, a problem-specific genetic algorithm (PSGA) with multiple population initialization rules and the route improvement strategy is introduced to balance the convergence speed and population diversity, achieving a promising planning result. In the route adjusting stage, various removal and insertion heuristics in the large neighborhood search (LNS) are designed to adjust the routes, thereby further improving the planning result. The routes participating in adjustment are obtained via the developed route selection rule. The validity of each stage in MSVR-TG has been verified in a series of scenarios. A comparison about the proposed algorithm against several advanced algorithms is constructed, and results reveal that MSVR-TG is superior over compared algorithms for VRPEC.

FC-Kmeans: Fixed-centered K-means algorithm

Clustering is one of the data mining methods that partition large-sized data into subgroups according to their similarities. K-means clustering algorithm works well in spherical or convex data distribution of large-sized data sets. Most of the algorithms based on K-means have generally been interested in an initial cluster centers selection or cluster distribution. However, these algorithms may not meet satisfy some requirements in practice. This paper presents the FC-Kmeans algorithm, which enables clustering by fixing some cluster centers considering real conditions. Thus, while some of the cluster centers are fixed, it is tried to obtain the most appropriate cluster centers for the others and the best distribution of the data to the clusters. The K-means clustering algorithm is compared with two different fixed-centered clustering algorithms which are FC-Kmeans and FC-Kmeans 2. The experimental results show that although the FC-Kmeans algorithm has more limitations than K-means, it converges the performance of K-means algorithm according to some performance indicators such as SSE, DB Index and Silhouette Index.

An Improved Clustering Algorithm Based on Density Peak and Nearest Neighbors

Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.

Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation

Glance behavior is significant because whether and how the driver is scanning and observing the driving scene is closely related to driving safety. This paper aims to improve the accuracy of glance behavior modeling and realize the spatiotemporal representation and visualization of glance behavior. Forty subjects were recruited to perform a freeway driving task using a driving simulator. The vehicle data were collected by the simulator. Drivers’ gaze points were collected by an eye tracker. The prior knowledge on gaze points obtained through a statistical analysis were provided for K-means (KM) to form a semi-supervised K-means (SSKM), which classifies gaze points into different fixation zones. The classification results were compared with the results of KM. Furthermore, a clustering center-based piecewise aggregate representation (CCPAR) was proposed to characterize glance behavior. Maneuvers identification was taken as a case to evaluate the proposed method. The k-nearest neighbour (KNN) based on the similarity of CCPAR identified driving maneuvers into lane-keeping, left lane change, and right lane change. The identification results were compared with the results of the Hidden Markov Model (HMM). The average classification accuracies of KM and SSKM were 55.28% and 94.75%, respectively. The accuracies of maneuvers identified by CCPAR-KNN and by HMM were 87.50% and 85.83%, respectively. The results indicate that SSKM and CCPAR are feasible for glance behavior modeling. SSKM eliminates the randomness of initial cluster center selection and improves the accuracy of gaze points classification. CCPAR is intuitive and convenient to describe and visualize the spatiotemporal characteristics of glance behavior.

A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree

Lithology identification methods based on conventional logging data are essential in reservoir geological evaluation. Due to the highly non-linear relationship between lithology and various logging parameters, conventional methods cannot meet the requirements. In recent years, machine learning methods such as Neural Networks and decision tree have been applied to the field of lithology identification and achieved good effects. However, there is no obvious difference in logging parameters for various types of lithology, and at the same time, there is a large amount of information redundancy between each logging curve. Therefore, its uncertainty and fuzziness are high, which interferes with the result of lithology identification. Combining fuzzy theory, decision tree and K-means++ algorithm, this paper proposes a novel hybrid technique of lithology identification which can better overcome the ambiguity and uncertainty of logging data. In the actual data test, we select six logging parameters: density (RHOB), neutron porosity (NPHI), natural gamma (GR), longitudinal wave velocity (VP), shallow formation resistivity (LLS), and deep formation resistivity (LLD). Then K-means++ clustering algorithm was used for clustering analysis on logging data. Finally, the triangular membership function is selected to fuzz the logging data according to the obtained clustering center points, and a fuzzy decision tree lithology identification model is constructed. The prediction accuracy of the model reached 93.92%. The fuzzy decision tree algorithm was also compared with five machine learning algorithms, including decision tree, extremely randomized trees (ET), Adaboost, random forest (RF) and gradient boosting decision tree (GBDT). The results show that the modeling results of fuzzy decision tree algorithm outperform other algorithms. In summary, the fuzzy decision tree model developed in the study is a practical and effective model for complex lithology identification, providing a new idea for lithology identification. • A fuzzy decision tree model using logging data for lithology identification is proposed. • The k-means++ algorithm optimizes the selection of initial clustering centers. • Logging data is fuzzily processed by the triangular membership function. • Pre-pruning the fuzzy decision tree by controlling the values of significance level α and truth degree threshold β. • The accuracy of the model can reach 93.92%, which is better than other algorithms.

A K-means Optimization Algorithm Suitable for Fast Clustering of WebGIS Massive Data

K-means has the advantage of fast speed and is suitable for clustering large-scale data of WebGIS geographic information. However, due to the random selection of K-means initial clustering centers, the clustering results are unstable and the clustering accuracy is poor. Some current research documents have solved the problems of clustering accuracy and stability, but the clustering time has been greatly increased. The article proposes a grid-based K-means improved algorithm GBK-means, which is based on an adaptive grid method to obtain initial clustering centers. Firstly, the parameters of the grid division are obtained by judging the distribution state of the sample data; then, the interconnected areas of each dense grid are obtained and the cluster centers are obtained, and on this basis, the initial cluster centers are obtained. The experimental results on the real data set of WebGIS show that GBK-means has better clustering effect and faster clustering speed than K-means, K-means++, literature [2], and literature [3]. The average value of its F value, accuracy rate and adjusted Rand coefficient (ARI) is 10.9%, 11% and 11.2% higher than that of K-means. The average clustering time is 75.4%, 52.3%, 85.1%, 91.1% faster than K-means, K-means++, literature [2], and literature [3].