Cluster Center Points Research Articles

ANALYSIS OF K-MEAN CLUSTERING IN GROUPING CHILD MORTALITY RATE IN INDONESIA

The death rate is an indicator of health status in society. One of these indicators is the Child Mortality Rate (CMR), which reflects environmental health conditions which directly influence children's health levels. The KMeans algorithm divides data into 3 groups (Clusters), namely Clusters 0, 1 and 2 to produce information on child mortality rates in all districts/cities in Indonesia. This modeling produces 3 clusters, namely cluster 0 (Districts/Cities with Medium Child Mortality Rates) with 124 Regencies/Cities, cluster 1 (Regency/Cities with High Child Mortality Rates) with 31 Regencies/Cities and cluster 2 (Regency/Cities with High Child Mortality Rates). Low Child Mortality) 391 Districts/Cities. This clustering produces a Davies Bouldin Index value of 0.493, the center point of Cluster 0 is 2.744, the center point of Cluster 1 is 6.185 and the center point of Cluster 2 is 13.756.

Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.

An improved K-Means clustering based on differential evolution

Randomly selecting the initial cluster center point in the K-means clustering algorithm can lead to the issue of local optima. The paper introduces an enhanced differential evolution algorithm for optimizing K-means clustering. Experimental results demonstrate the superiority of the improved K-means clustering, based on the proposed differential evolution, over other comparison algorithms. Notably, the optimized version exhibits exceptional performance in clustering Iris, Wine, and Glass datasets. The initial clustering center can improve the clustering accuracy in the later stage. Therefore, the proposed algorithm is more superior.

Harris’ Hawks Optimization-Tuned Density-based Clustering

Clustering is a machine learning technique that groups data samples based on similarity and identifies outliers with distinct features. Density-based clustering outperforms other methods because it can handle arbitrary shapes of clustering distributions. However, it has a limitation of requiring empirical values for the cluster center and the nominal distance between the cluster center and other data points. These values affect the accuracy and the number of clusters obtained by the algorithm. This paper proposes a solution to optimize these parameters using Harris’ hawks optimization (HHO), an efficient optimization technique that balances exploration and exploitation and avoids stagnation in later iterations. The proposed HHO-tuned density-based clustering achieves better performance as compared to other optimizers used in this work. This research also provides a reference for designing efficient clustering techniques for complex-shaped datasets.

An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents

The timely and scientific assessment of railway accident severity can provide effective support for making a suitable rescue plan. This paper proposes an improved density peaks clustering algorithm based on scenario construction for dynamically tackling the severity ranking problem of railway accidents. Firstly, the scenario is introduced into the selection of grading indicators of railway accidents, and a five-dimensional railway accident scenario expression model based on knowledge element is proposed to dynamically select and express the grading indicators. Secondly, an improved density peaks clustering algorithm based on k nearest neighbors and turning point (IDPC-KNN-TP) as the grading model of railway accidents is established, which optimizes the local density measurement, the selection of cluster center points, and the assignment strategy of density peaks clustering (DPC). Finally, the railway accidents from 2010 to 2021 in the UK as a case study are analyzed. The results show that the performance of IDPC-KNN-TP is better than others, and the grading algorithm in this paper has good practical performance. The dynamic grading strategy and the emergency disposal suggestions are put forward to provide a reference for the hierarchical response to railway accidents.

Implementation of K-Means Algorithm with Clustering Method to Data Management of Covid-19 Case in West Bandung Using Orange Application

Covid-19 cases in Indonesia have not disappeared to the until now. Coronavirus has spread to almost all provinces in Indonesia, one of the province is West Java. There are 27 regencies/cities in West Java that have become the spread of the Covid-19 case, one of which is West Bandung Regency. To make it easier for the regional government of West Bandung Regency to take action in an effort to prevent the spread of Covid-19, research is needed to determine the level of distribution of Covid-19 cases which are divided into 3 clusters, namely high, medium and low clusters. Researchers analyzed using the data mining method with the K-Means Clustering algorithm through the Orange Application. Data processing is started from determining amount of the data to be clustered into 3 high, medium and low, then determining the initial centroid cluster center point (iteration 1) that has been determined randomly, then calculating the distance of each data to the center of the cluster between objects to closest centroid. The distribution of cluster 1, cluster 2, and cluster 3 as many as 16 data in the K-Means Clustering process using the Orange Application. The research results show that there are 8 sub-districts for high clusters, 2 sub-districts for medium clusters and 6 sub-districts for low clusters in West Bandung Regency.

Failure Evaluation of Electronic Products Based on Double Hierarchy Hesitant Fuzzy Linguistic Term Set and K-Means Clustering Algorithm

The extent of failure evaluation depends on the complexity and significance of electronic products. However, traditional failure mode and effect analysis (FMEA) has many shortcomings, which brings large difficulty to failure evaluating work. This paper uses the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) and the K-means clustering algorithm to improve the shortcomings of traditional FMEA. The DHHFLTS is a set of language terms based on the symmetry method and uniform language granularity. Firstly, we determine the product failure mode and set up an evaluation team after formulating an evaluation symmetrical language set. The psychological changes of the evaluators can be truly expressed by using the DHHFLTS. Secondly, the entropy weight method is used to calculate the weight of the evaluation members. The evaluation information of the evaluation personnel on the failure mode is aggregated based on the weight of the evaluation members. Then, the K-means clustering algorithm is used to calculate the distance between failure modes and each cluster center point by using the normalized weight of influencing factors and the evaluation distance of each evaluator. Finally, the evaluation of an electromagnet failure mode is taken as an example to prove the objectivity and practicability of the new method.

Initial Cluster Centers Based on Moving Two Lines Approximation in K-means Algorithm

The main shortcoming of K-means clustering algorithm is its great dependence on the initial cluster center point. Based on the moving two lines approximation model, this paper gives a method to pick the initial cluster center of k-means clustering. Numerical experiments and comparison criteria show that this method can get better clustering effect.

Design of Iterative Fuzzy Radial Basis Function Neural Networks Based on Iterative Weighted Fuzzy C-Means Clustering and Weighted LSE Estimation

In this article, a reinforced iterative fuzzy radial basis function neural networks (IFRBFNN) is introduced as an augmented FRBFNN architecture generated through the iterative refinement process of weighted fuzzy C-means (WFCM) clustering and weighted least square error estimation. It is well known that the location of the clusters have an effect on the classification performance of the FRBFNN. The underlying idea behind this article is how to define the center points of clusters based on the data distribution analysis as well as the improvement of classification performance. In a nutshell, while the fuzzy C-means clustering to define the positions of radial basis functions (RBFs) based in the unsupervised learning manner is usually used in a FRBFNN, the parameters (cluster centers and their ensuing polynomial coefficients) refinement of FRBFNN through the proposed iterative method lead to superb classification performance by relocating the positions of RBFs over the input space implied by supervised learning. The idea of the proposed approach is to relocate the centers (prototypes) of the fuzzy clusters by using WFCM clustering algorithm and to re-estimate the coefficients by weighted least square estimation with the aid of the cross-entropy loss values of data so that the classification performance is improved. The weight related to each data is determined by some auxiliary information (i.e., the modified version of the cross-entropy loss value of each data). When it comes to the estimation of the coefficients of the consequent polynomial in IFRBFNN, they are estimated by the weighted least square error estimation technique, where the weights of the coefficients are defined based on the cross-entropy loss values. The auxiliary information such as the weights for relocation of the centers of RBFs and the weights for the re-estimation of the polynomial coefficients should be defined from the viewpoint of enhancement of the classification performance. The cross-entropy loss value of each data is able to meet the necessity of the required supervision signal. Several numerical experiments are provided to demonstrate the usefulness of the proposed design method for the FRBFNN for classification problems.

METODE K-HARMONIC MEANS CLUSTERING DENGAN VALIDASI SILHOUETTE COEFFICIENT (Studi Kasus : Empat Faktor Utama Penyebab Stunting 34 Provinsi di Indonesia Tahun 2018)

The k-harmonic means method is a method of using the cluster center point value, which is to determine each cluster from its center point based on the calculation of the harmonic average. The k-harmonic means determines the existence of each data point based on the membership function and weighting function by using a distance measure. in the clustering, which aims to increase the importance of data that is far from each central point. This causes the k-harmonic means to be insensitive in initialization in determining the cluster center point and significantly improves the quality of clustering compared to k-means. In determining the level of similarity, the determination of the level of similarity uses the distance measure and the distance measure used is the Euclidean distance measure. The distance measure used in cluster analysis can affect the cluster results obtained. Thus, to determine the quality of the results of the cluster analysis, validation tests were carried out using an internal criteria approach, namely silhouette coefficient. In this study, the k-harmonic means used to classify provinces in Indonesia based on the causes of stunting in 2018. The stunting in children under five in Indonesia has exceeded the limit set by WHO. In 2016-2017 there was an increase in the prevalence of stunting by 27.5% to 29.6%. The k-harmonic means method is used so that the four main factors causing stunting in every province in Indonesia can be seen and the prevention and cure of stunting can run optimally. This method is also used because the data on the four factors that cause stunting show a significant rate of change and as a measure of central tendency in 34 provincial objects in Indonesia. Four factors that cause stunting are used, namely the percentage of households that do not have access to clean drinking water, the percentage of exclusive breastfeeding, the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities. The results obtained by the cluster which is optimal at k= 3 using the Euclidean, where the silhouette coefficient = 0,3040722675 ≈ 0,3. Based on the results of the cluster analysis, it is known that in cluster one, the main factor that stands out the most is the percentage of exclusive breastfeeding. In cluster two, the main factor that stands out the most is the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely. In cluster three, the most prominent main factors are the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities with the highest average centroid among other clusters. Keywords: Clustering, K-Harmonic Means, Euclidean distance, Silhouette Coefficient, Stunting

High-Precision Motion Compensation for LiDAR Based on LiDAR Odometry

LiDAR plays a pivotal role in the field of unmanned driving, but in actual use, it is often accompanied by errors caused by point cloud distortion, which affects the accuracy of various downstream tasks. In this paper, we first describe the feature of point cloud and propose a new feature point selection method Soft-NMS-Select; this method can obtain uniform feature point distribution and effectively improve the result of subsequent point cloud registration. Then, the point cloud registration is completed through the screened feature points, and the odometry information is obtained. For the motion distortion generated in a sweep, the prior information of the LiDAR’s own motion is obtained by using two linear interpolations, thereby improving the effect of motion compensation. Finally, for the distortion caused by the motion of objects in the scene, Euclidean clustering is used to obtain the position and normal vector of the center point of the point cloud cluster, and the motion pose of the object is calculated according to the offset between adjacent sweeps and eliminated distortion. Essentially, our method is a general point cloud compensation method that is applicable to all uses of LiDAR. This paper inserts this method into three SLAM algorithms to illustrate the effectiveness of the method proposed in this paper. The experimental results show that this method can significantly reduce the APE of the original SLAM algorithm and improve the mapping result.

The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm

Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a very dangerous impact as well as economic losses for farmers. The characteristics that are very visible on rice leaves have a greater area than other plant structures, rice leaves can be applied for early diagnosis of rice plant diseases. Fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means are the approaches employed (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the secondary dataset is UCI Rice Leaf Diseases. According to the results of the comparison the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhoutte coefficient value compared to just 60% for FCM. This research shows the results that the proposed method can add 5% accuracy to the clustering results in terms of identifying the types of rice plant diseases properly.

K-Means Performance Optimization Using Rank Order Centroid (ROC) And Braycurtis Distance

K-Means is a clustering algorithm that groups data based on similarities between data. Some of the problems that arise from this algorithm are when determining the center point of the cluster randomly. This will certainly affect the final result of a clustering process. To anticipate the poor accuracy value, a process is needed to determine the initial centroid in the initialization process. The second problem is when calculating the Euclidean distance on the distance between data. However, this method only gives the same impact on each data attribute. From some of these problems, this study proposes the Rank Order Centroid (ROC) method for initializing the cluster center point and using the Braycurtis distance method to calculate the distance between data. With the experiment K=2 to K=10, the results obtained in this study are the proposed method obtains an iteration reduction of 6.6% on the Student Performance Exams dataset and 19.3% on the Body Fat Prediction dataset. However, there was an increase in iterations on the Heart Failure dataset by 24.2%. In testing the cluster results using the Silhouette Coefficient, this method shows an increase in the evaluation value of 5.9% in the Student Performance Exams dataset. However, the evaluation value decreased by 8.3% in the Body Fat Prediction dataset and 3.3% in the Heart Failure dataset.

Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis.

How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain “outliers,” and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation.

Penerapan Data Mining Dalam Pengelompokan Data Member Card Mitra10 Untuk Meningkatkan Rewards Terhadap Konsumen dengan Metode Fuzzy Subtractive Clustering

Data clustering of Mitra10 member card, it is only based on the payment day by customer. And the total point only based on 0,25% calcutation of each payment. This is not so efficient knowing that there are so many ungrouping data that can be use for increase the rewards to the customer. In order to clustering the data of member card total point, it need a method of data clustering. The method election really affecting the result of data clustering. After all of the member card Mitra10 data transformed to the number form, then the datas can be grouping to fuzzy subtractive clustering algorythm. The data need to divided to some cluster : Select the amount of the cluster. In this research the data will be divided to 3 cluster. Select the center point of each cluster and fisrt center point selected randomly to generate main center point of each cluster. One data will be part of one cluster that has the smallest distance from the center cluster. Example for the first data, smallest distance get by cluster 1, so that the first data will be member of cluster 1. And so for the second data , smallest distance in cluster 3, so it will be member of cluster 3. In this iteration, center point of each cluster does not changed and there is no moving data to another cluster. Cluster 1 result: Has center (1,413, 1,195, 1,695) cluster 1 dominated by the bronze group. Cluster 2 result: Has center (2,658, 1,219, 1,585) cluster 2 dominated by the silver group. Cluster 3 result: Has center (4, 1,8, 3) cluster 3 dominated by the silver group

Simplification algorithm of denture point cloud based on feature preserving

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

Research on Density-Based K-means Clustering Algorithm

Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.

A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function.

Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process. The fuzzy C-means clustering of differential privacy protection can protect the user’s individual privacy while mining data rules, however, the decline in availability caused by data disturbances is a common problem of these algorithms. Aiming at the problem that the algorithm accuracy is reduced by randomly initializing the membership matrix of fuzzy C-means, in this paper, the maximum distance method is firstly used to determine the initial center point. Then, the gaussian value of the cluster center point is used to calculate the privacy budget allocation ratio. Additionally, Laplace noise is added to complete differential privacy protection. The experimental results demonstrate that the clustering accuracy and effectiveness of the proposed algorithm are higher than baselines under the same privacy protection intensity.