Original K-means Algorithm Research Articles

Underwater small target detection based on dynamic convolution and attention mechanism

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

Stratification-based semi-supervised clustering algorithm for arbitrary shaped datasets

Semi-supervised clustering is not only an important branch of semi-supervised learning but also an improvement direction for clustering. Semi-supervised clustering algorithms designed based on Kmeans, such as the classical Seeded-Kmeans and Constrained-Kmeans, where supervision information is used to guide clustering iterations, have the same disadvantages as the original Kmeans algorithm: they are confined to the assumption of isotropic spherical clusters, leading to the narrow adaptability in handling data of various characteristics. To solve the problem, we propose the scattered centroids initialization clustering algorithm based on Stratification (SCICS). First, based on the concept of influence space, a method for modeling the cluster-level location of any object is presented, according to which we can obtain well-defined cluster decision boundaries through stratification. On this basis, by extending the seed thought, we propose a semi-supervised subclustering algorithm that can break through the limitations of partitional clustering methods that rely on strict assumptions on particular cluster distributions. Experiments on artificial and real-world datasets show that the proposed algorithm gains the ability of clustering arbitrary shaped data and surpasses the competitors in terms of performance and adaptability.

Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

To address the problems of error and omission detection in remote sensing image detection caused by the diverse scale changes of remote sensing object scales and the abundant proportion of small-scale objects, as well as the global and dense distribution of remote sensing objects, a remote sensing image detection improvement method based on YOLOv5-S is proposed. First, according to the characteristics of remote sensing objects, the data enhancement strategy is adopted to expand the dataset samples for the characteristics of remote sensing objects to improve the generalization ability of the model. Second, the contextual transformer module is introduced to the backbone feature extraction network and the feature fusion network to ensure the local feature extraction capability while improving the global information acquisition capability of the model, making full use of the input contextual information and guiding the dynamic attention matrix learning to improve the visual representation ability. Third, based on the original model, a shallow detection scale is added, and then a multiscale complex fusion structure is adopted. Meanwhile, the K-means++ algorithm replaces the original K-means algorithm and then clusters 12 anchor box sizes. Fourth, the efficient intersection over union loss is used to improve the accuracy of the remote sensing object recognition prediction. In the experiment on the on two optical remote sensing image datasets, a comparison with several object detection algorithms based on convolutional neural network is made, the results show that the mAP@0.5 tested on the remote sensing datasets is higher than the original YOLOv5-S. Compared with other models, the detection efficiency is higher, and the problems of small-scale object detection in remote sensing image have been significantly improved.

Using K-Means Clustering in Python with Periodic Boundary Conditions

Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

Clustering Algorithm for Big Datasets with Mixed Attribute Features under Spark

This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments.

Research on road vehicle and lane line detection based on improved YOLOv4-tiny algorithm

In order to improve the speed of road vehicle and lane line detection, a road vehicle and lane line detection method with improved YOLOv4-tiny algorithm is proposed by using K-means++ clustering algorithm instead of the original K-means algorithm. Using the Mosaic data enhancement method, four images are randomly deflated and then stitched into one image to enrich the detection target background. The optimal weight values are derived by multi-scale training using the GPU through image feature extraction of the BDD10K dataset to achieve the detection of vehicle and lane line targets in the images. The results show that the improved YOLOv4-tiny algorithm achieves a detection speed of 134 FPS and an average accuracy of 77.84% mAP in highway lane line detection. After comparison, the detection speed of the improved algorithm is significantly improved, effectively improving the efficiency of highway vehicle and lane line detection.

Analysis of the Practice Path of the Flipped Classroom Model Assisted by Big Data in English Teaching

This paper makes a detailed analysis of the integrated mining algorithm, analyzes the characteristics of curriculum big data, and analyzes the existing problems of the current association rule mining algorithm, as well as the defects and deficiencies when applied to the curriculum data. Aiming at the problem of mining the entire data set by the mining algorithm, this topic proposes the idea of using the K-means algorithm for clustering processing and uses the Ball-tree structure on the basis of the original K-means algorithm to improve the efficiency of the algorithm. The data set is separated into several clusters of an appropriate number. In the flipped classroom, the basic knowledge is put before the class for learning, and the further deepening and practical application of language knowledge is completed in the class. Teachers can give timely guidance when encountering unsolvable difficulties so that students’ learning can be more effective. This new teaching model not only strengthens students’ confidence in learning and increases their interest in learning, but also increases the opportunities for students to interact with teachers and classmates in the classroom, allowing them to construct the meaning of knowledge in the fun of interactive communication. The classroom has become relaxed, lively, and attractive, and students’ sense of autonomy, self-learning ability, and collaborative inquiry ability have also been unknowingly improved. Among the main factors, the willingness to flip, emotional state, leadership role, and online learning input have a significant positive impact on collaborative learning performance, and the sense of competition has a partial negative impact on collaborative learning performance, of which positively affecting individual knowledge mastery. Among the nonmain factors, the degree of difficulty of the course, teacher-student interaction, teacher motivation, and evaluation mechanism have a significant positive impact on collaborative learning performance. Classroom assistive technology has a partial negative impact on collaborative learning performance. From the perspective of group performance, group-level performance considerations such as the quality of group conversations and the degree of group knowledge sharing in collaborative learning performance are more affected by nonsubject factors.

Single-cell transcription group sequencing and the application of artificial intelligence in developmental biology

In the past two or three years, genome sequencing technology has been rapidly developed. Large-scale sequencing projects such as the Human Genome Project and the Cancer Genome Project have been launched one after another. Up to now, due to the emergence and research of artificial intelligence, it has brought us many possibilities. The purpose of this article is to use artificial intelligence to help single-cell transcription sequencing as much as possible. Based on the idea of Euclid algorithm, an improved K-means algorithm is proposed, which to a certain extent avoids the phenomenon of clustering results falling into local solutions, and reduces the appearance of the original K-means algorithm due to the use of error squares criterion function. In the case of dividing large clusters, the simulation experiment results show that the improved K-means algorithm is better than the original algorithm and has better stability.

Design of smart agriculture based on big data and Internet of things

With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Learning Management System (LMS) is well de-signed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student’s learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student’s learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering

The search algorithm C-LCA is done by adding two chaotic parameters into the LCA.The search clustering using CS-LCA and the KSC-LCA algorithms are proposed.The Gower distance and a mechanism are adopted for handling the mixed-type data.The search clustering CS-LCA ranks first for the pure categorical data.The KSC-LCA ranks first for the pure numeric and mixed-type data. The success rates of the expert or intelligent systems depend on the selection of the correct data clusters. The k-means algorithm is a well-known method in solving data clustering problems. It suffers not only from a high dependency on the algorithm's initial solution but also from the used distance function. A number of algorithms have been proposed to address the centroid initialization problem, but the produced solution does not produce optimum clusters. This paper proposes three algorithms (i) the search algorithm C-LCA that is an improved League Championship Algorithm (LCA), (ii) a search clustering using C-LCA (SC-LCA), and (iii) a hybrid-clustering algorithm called the hybrid of k-means and Chaotic League Championship Algorithm (KSC-LCA) and this algorithm has of two computation stages. The C-LCA employs chaotic adaptation for the retreat and approach parameters, rather than constants, which can enhance the search capability. Furthermore, to overcome the limitation of the original k-means algorithm using the Euclidean distance that cannot handle the categorical attribute type properly, we adopt the Gower distance and the mechanism for handling a discrete value requirement of the categorical value attribute. The proposed algorithms can handle not only the pure numeric data but also the mixed-type data and can find the best centroids containing categorical values. Experiments were conducted on 14 datasets from the UCI repository. The SC-LCA and KSC-LCA competed with 16 established algorithms including the k-means, k-means++, global k-means algorithms, four search clustering algorithms and nine hybrids of k-means algorithm with several state-of-the-art evolutionary algorithms. The experimental results show that the SC-LCA produces the cluster with the highest F-Measure on the pure categorical dataset and the KSC-LCA produces the cluster with the highest F-Measure for the pure numeric and mixed-type tested datasets. Out of 14 datasets, there were 13 centroids produced by the SC-LCA that had better F-Measures than that of the k-means algorithm. On the Tic-Tac-Toe dataset containing only categorical attributes, the SC-LCA can achieve an F-Measure of 66.61 that is 21.74 points over that of the k-means algorithm (44.87). The KSC-LCA produced better centroids than k-means algorithm in all 14 datasets; the maximum F-Measure improvement was 11.59 points. However, in terms of the computational time, the SC-LCA and KSC-LCA took more NFEs than the k-means and its variants but the KSC-LCA ranks first and SC-LCA ranks fourth among the hybrid clustering and the search clustering algorithms that we tested. Therefore, the SC-LCA and KSC-LCA are general and effective clustering algorithms that could be used when an expert or intelligent system requires an accurate high-speed cluster selection.

MODIFIED SELECTION OF INITIAL CENTROIDS FOR K- MEANS ALGORITHM

This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable result of clustering the data. The modified approach of the k-means algorithm integrates the computation of the weighted mean to improve the seeds initialization. This paper shows the comparison of K-Means and Modified K-Means algorithm, using the first simple dataset of four objects and the dataset for service vehicles. The two simple applications proved that the Modified K- Means of selecting initial centroids is more reliable than K-Means Algorithm. Clustering is better achieved in the modified k-means algorithm. Article DOI: http://dx.doi.org/10.20319/mijst.2016.22.4864 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

The Application of User Behavior Analysis by Improved K-means Algorithm Based on Hadoop

In recent years, the new social network and mobile Internet technology promote the rapid growth of the number of network users, as well as the network data showes explosive growth.How to analysisand extract the users behavior characteristics from the mass data mining is more and more important. There are many clustering algorithms for user behavior analysis, and K-means algorithm is one of the most common method. However, there are some disadantages of K-means that lead to reduce the performance of clustering, which include that the number of clusters and the cluster center must be initialized, it issensitive to abnormal data, andit can only handle the numerical data.Based on the research of Hadoop, and clustering algorithms, we propose a K-means clustering method based on Canopy method.Finally, we carried out a comparative analysis of results of cluster experiment by comparing to the single machine experiment.The experimental results show that the K-means clustering algorithm based on Canopy is faster than the original K-means algorithm, which means that the algorithm has better expansibility, and it has a high application value.

A Non-symmetrical Weighted K-means with Rank-Based Artificial Bee Colony Algorithm for Medical Diagnosis

This study proposes a non-symmetrical weighted k-means (NSWKM) clustering algorithm to improve the accuracy of clustering result. The similarity distance of original k-means algorithm is modified by adding weights with a non-symmetrical form to the distance measurement. Namely, different weights for attributes are applied for clusters such that the contribution of attributes can be adjusted adaptively during the clustering process. In this work, the weights are given via an optimization process using a rank-based artificial bee colony (RABC) algorithm. Furthermore, the proposed NSWKM clustering algorithm combined with the RABC, termed NSWKM-RABC herein, is then applied to the medical diagnoses of five data sets of diseases, including breast cancer, cardiac disease, diabetes, liver disease and hepatitis, to evaluate the performance of the proposed algorithm.

FUZZY C-MEANS ALGORITHM FOR SEGMENTATION OF AERIAL PHOTOGRAPHY DATA OBTAINED USING UNMANNED AERIAL VEHICLE

Abstract. The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

K-means algorithms for functional data

Cluster analysis of functional data considers that the objects on which you want to perform a taxonomy are functions f:X⊂Rp↦R and the available information about each object is a sample in a finite set of points fn={(xi,yi)∈X×R}i=1n. The aim is to infer the meaningful groups by working explicitly with its infinite-dimensional nature.In this paper the use of K-means algorithms to solve this problem is analysed. A comparative study of three K-means algorithms has been conducted. The K-means algorithm for raw data, a kernel K-means algorithm for raw data and a K-means algorithm using two distances for functional data are tested. These distances, called dVn and dϕ, are based on projections onto Reproducing Kernel Hilbert Spaces (RKHS) and Tikhonov regularization theory. Although it is shown that both distances are equivalent, they lead to two different strategies to reduce the dimensionality of the data. In the case of dVn distance the most suitable strategy is Johnson–Lindenstrauss random projections. The dimensionality reduction for dϕ is based on spectral methods.A key aspect that has been analysed is the effect of the sampling {xi}i=1n on the K-means algorithm performance. In the numerical study an ex professo example is given to show that if the sampling is not uniform in X, then a K-means algorithm that ignores the functional nature of the data can reduce its performance. It is numerically shown that the original K-means algorithm and that suggested here lead to similar performance in the examples when X is uniformly sampled, but the computational cost when working with the original set of observations is higher than the K-means algorithms based on dϕ or dVn, as they use strategies to reduce the dimensionality of the data.The numerical tests are completed with a case study to analyse what kind of problem the K-means algorithm for functional data must face.

An Improved K-Means Algorithm and its Application in Customer Classification of Network Enterprises

K-means algorithm has powerful ability to cluster large data sets due to its high efficiency in data mining but its calculation instability limits the application of the algorithm, so the research of intelligent optimization of K-means algorithm has become a hot research field for the researchers related. First the calculation instability of the original K-means algorithm is analyzed with more details; Second, the improvement of cluster seed selection methods and the calculation flow of K-means algorithm are redesigned to speed up the calculation and enhance the stability of the improved model; Third, the paper realizes and conducts the analysis in customer classification practice of the improved algorithm which show that the improved K-means algorithm has better performance in classification accuracy and calculation stability and can be used in customer classification for network trade enterprises practically.

Comparative Study of Clustering Algorithms Used in Counter Terrorism

Data mining can be used to model crime detection problems, detect unusual patterns, terrorist activities and fraudulent behaviour. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. The k-means algorithm is one of the frequently used clustering method in data mining, due to its performance in clustering massive data sets. The final clustering result of the k-means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. The original k-means algorithm converges to local minimum, not the global optimum. Many improvements were already proposed to improve the performance of the k-means, but most of these require additional inputs like threshold values for the number of data points in a set. In this paper a new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity.

Application of Improved K-Means Clustering Algorithm in Customer Segmentation

Market competition is the competition for customers. By adopting customer segmentation model, decision makers can effectively identify valuable customers and then develop effective marketing strategy. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroids. An improved K-means algorithm is presented,with which K value of clustering number is located according to the clustering objects distribution density of regional space,and it uses centroids of high-density region as initial clustering center points. The proposed method makes the algorithm more effective and efficient, so as to gets better clustering with reduced complexity.

Mine Blood Donors Information through Improved K-Means Clustering

The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessityfor the organized analysis of the blood donor database or blood banks repositories. Cluste ring analysis is one of the data mining applications and K -means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original kmeans algorithm by improving the initial centroids with distribution of data. Results and discussions show that impro ved K-means algorithm produces accurate clusters in less computation time to find the donors information.