Selection Of Initial Cluster Centers Research Articles

An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN.

Book localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional object detection algorithms. To address these challenges, this study proposes an enhanced oriented R-CNN algorithm for book spine detection. First, we replace the standard 3 × 3 convolutions in ResNet50's residual blocks with deformable convolutions to enhance the network's capacity for modeling the geometric deformations of book spines. Additionally, the PAFPN (Path Aggregation Feature Pyramid Network) was integrated into the neck structure to enhance multi-scale feature fusion. To further optimize the anchor box design, we introduce an adaptive initial cluster center selection method for K-median clustering. This allows for a more accurate computation of anchor box aspect ratios that are better aligned with the book spine dataset, enhancing the model's training performance. We conducted comparison experiments between the proposed model and other state-of-the-art models on the book spine dataset, and the results demonstrate that the proposed approach reaches an mAP of 90.22%, which outperforms the baseline algorithm by 4.47 percentage points. Our method significantly improves detection accuracy, making it highly effective for identifying book spines in real-world library environments.

Antarctic Sea ice distribution detection based on improved ant colony algorithm

The changes in the Antarctic sea ice area are directly related to the changes in the atmosphere and oceans. Determining the Antarctic sea ice distribution is of great significance to the global climate change analysis. The ant colony algorithm adopts a positive feedback mechanism to continuously converge the search process and ultimately approaches the optimal solution, making it easy to find the optimal segmentation threshold for detecting the sea ice distribution. However, the ant colony algorithm has the problems of high computational complexity and easy getting stuck in local optima. In order to better apply the ant colony algorithm to sea ice distribution detection, an improved ant colony algorithm was proposed, which improves the selection of initial clustering centers and the update of pheromone volatilization factors in the ant colony algorithm. We compared the improved ant colony algorithm with iterative algorithm, maximum entropy algorithm, and basic global threshold algorithm, and the results showed that the proposed algorithm is feasible. To further validate the accuracy of the improved ant colony algorithm, we compared the results obtained from MODIS data with the improved ant colony algorithm, iterative algorithm, maximum entropy algorithm, and basic global threshold algorithm for sea ice detection, and the results showed that the accuracy of the proposed algorithm was 4.99%, 3.66%, and 5.46% higher than the other three algorithms, respectively.

A Review on the Application of Automatic Text Summarization and Its Bias Analysis

Abstract. In the era of information explosion, the Internet produces a large number of text documents daily, providing rich information but also posing a challenge: how to swiftly extract key information. Traditional manual reading is time-consuming and inadequate for handling vast data. Thus, automatic text summarization technology emerges as a crucial solution. This paper reviews the application and deviation analysis of this technology across various fields, focusing on addressing shortcomings of traditional methods, such as initial cluster center selection and redundancy. An automatic text summarization method based on an improved TextRank algorithm and K-Means clustering is introduced. Existing methods often struggle with inaccurate initial clustering center selection and high summary redundancy, especially with long texts, resulting in summaries that fail to reflect core content accurately. Furthermore, the widespread use of pre-trained language models introduces potential biases that can propagate to downstream tasks, affecting summary accuracy and impartiality. To address these issues, this paper proposes an innovative automatic text summarization method that optimizes initial clustering center selection and clustering refinement strategies to enhance summary accuracy and readability. Additionally, it discusses name-nationality bias in pre-trained language models and its propagation in text summary tasks, offering a theoretical foundation and practical guidance for developing a more just and reliable Natural Language Processing (NLP) system.

Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood Information

This paper presents an intuitionistic fuzzy c-means-based clustering algorithm for multi-view clustering, addressing key challenges such as noise sensitivity, outlier influence, and the distinct importance of views, features, and samples. Our proposed approach incorporates view weights, feature weights, sample weights, and neighborhood information into a novel objective function. Additionally, we introduce an effective initial cluster center selection strategy that enhances clustering robustness. The efficiency of the proposed method is evaluated using various clustering criteria (AR, NMI, RI, FMI, and JI). Moreover, the effect of each module of the algorithm on the general clustering performance is examined exclusively. Experimental results on various benchmark multi-view datasets demonstrate that our algorithm outperforms state-of-the-art methods in terms of clustering accuracy and stability. The source code of the proposed method is accessible at https://github.com/Amin-Golzari-Oskouei/VCoFWMVIFCM.

A fuzzy K-Means algorithm based on Fisher distribution for the identification of rock discontinuity sets

Rock discontinuities significantly impact the mechanical and hydraulic behavior of rock masses. A crucial aspect of rock engineering involves classifying discontinuities with similar orientations into groups. For this purpose, clustering algorithms, such as K-Means and Fuzzy K-Means (FKM), have been employed. However, the outcomes of these algorithms are influenced by the selection of initial cluster centers. This paper proposes an improved FKM algorithm to automatically identify rock discontinuity sets based on the Fisher distribution (FFKM). The algorithm uses the Fisher distribution to generate and select appropriate initial cluster centers. The performance of FFKM was initially validated using a published data set, and its results were compared with other clustering methods commonly used for grouping discontinuities. Results demonstrated the superior performance of FFKM over the FKM algorithm, comparable to other methods. Subsequently, the proposed algorithm was employed to analyze a fracture data set sampled at an open-pit iron mine. The FFKM facilitated identifying the correct number of sets and produced results consistent with the fracture sets observed in the field. Finally, the algorithm was verified using an artificial discontinuity data set, and the results demonstrated that the method correctly identified the number of sets and provided discontinuity sets similar to the original data set. The FFKM algorithm offers significant advantages: it maintains the essential characteristics of the FKM algorithm, effectively addresses the challenge of selecting suitable initial cluster centers, requires only the expected number of discontinuity sets as an input parameter, processes data within an acceptable computation time, serves as a tool for defining the number of discontinuity sets, and mitigates the drawbacks of the stereographic projection method.

Behavioral analysis of electricity consumption characteristics for customer groups using the k-means algorithm

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.

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.