Clustering Of Objects Research Articles

An experimental approach to estimating the current oil recovery coefficient using a complex of stochastic and numerical computer modeling methods

This paper presents an experimental study devoted to determining the prospects for the integrated application of stochastic and numerical computer modeling methods to solve one of the fundamental problems of field development – assessing current oil recovery based on various parameters that reflect the properties of productive formations and their saturating fluids. The object of the study was the long-term deposits of the Volga–Ural oil and gas province. As part of the initial stage, a discriminant analysis was carried out according to the tectonic-stratigraphic criterion, as a result of which three clusters of homogeneous objects were formed based on seventeen parameters. When interpreting the distribution of deposits in the axes of canonical discriminant functions, the unstable parameter of the total thickness of the reservoir was replaced by one of the variable indicators selected using the digital implementation of the uniform search method and its interpretation using known approaches. In order to reduce the influence factor of the uncertainty zone that arises when considering objects in the axes of canonical discriminant functions, the method of constructing structured grids was used. The reliability and scope of the obtained dependence were established using the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The conclusion is made about the possibility of using the developed scientific and methodological approach to forecasting oil recovery at different values of the well grid density. Keywords: oil recovery coefficient; modeling of nonlinear systems; deposits of the Volga-Ural oil and gas province; tectonic and stratigraphic confinement of objects; stochastic and numerical methods of computer analysis and data processing; development of oil fields, geological and field parameters.

Automated identification and counting of saigas (<i>Saiga tatarica) by using deep convolutional neural networks in high-resolution satellite images

We utilized a two-phase analysis using deep convolutional neural networks (DCNN) to create an automated technology that enabled us to detect and count saigas (Saiga tatarica) in satellite images with a resolution of 0.3–0.5 m/pixel (Eros-B 2012; 2013 and Beijing KA 2022 satellites). In the first phase, the satellite image is automatically divided into sections and checked for the presence or absence of clusters of objects (the “classification” phase). Then, during the second phase, only the fragments of the satellite image where at least one saiga was previously found are analyzed (the “detection” phase). The method was calibrated by training a neural network on the results of the preliminary processing of archival satellite images from 2012 and 2013, carried out manually by zoological experts. When we tested the DCNN work with a “confidence threshold” of 0.3, we identified 1,284 saigas on the entire model satellite image, while a zoological expert manually identified 1,412 saigas. For practical use and to assess the effectiveness of this method, we counted saigas on a 2022 image covering two adjacent specially protected natural areas (PAs) located in the Republic of Kalmykia and the Astrakhan region (Russian Federation). The results are presented with different “thresholds of confidence”.

An improved Fuzzy multiple object clustering in remodeling of roofs with perceptron algorithm

The novel way is completely discrete technique to remodel the roof of old buildings from the real value related repetition. Pointed quantities of partition for most of the developing renovation or modifications at the roof via a strategy are some parameters simplifying a fuzzy multiple object technique, where every segment is linked to all clusters with specialized matching weights of roof segments. The strategy considers multiple objects of perceptron algorithm across the indepth joints and incorporates of 3 layers: (i) every part is featued with the leading direction of a vector of exact measures of the roof densities, (ii) most required factor of evaluation is completed to review the principle changes in lowing the outcomes of the clatter, and (iii) the squared Euclidean location most of the number one retained major components is used to c-arry out clustering through the equal vintage fuzzy Multiple object-approach using perceptron method. A perceptron technique, multiple object is applied strategy in this research and the image parts and its neighboring segments are added to form a cluster by using the use of immediate computation of the resulting fuzzy number and overall idea of the process of the technique is to compute the mapping concept of sequentially located from equally well-defined clusters. The fuzzy number is applied to multiple objects using perceptron algorithm of the segment and compared with fuzzy technique. The outcome is to bring the nearest neighbor of the fuzzy value,.

Research on Multimodal Control Method for Prosthetic Hands Based on Visuo-Tactile and Arm Motion Measurement.

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot's grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability.

Spatially coherent 3D distributions of HI and CO in the Milky Way

The spatial distribution of the gaseous components of the Milky Way is of great importance for a number of different fields, for example, Galactic structure, star formation, and cosmic rays. However, obtaining distance information to gaseous clouds in the interstellar medium from Doppler-shifted line emission is notoriously difficult given our vantage point in the Galaxy. It requires spatial knowledge of gas velocities and generally suffers from distance ambiguities. Previous works often assumed the optically thin limit (no absorption), had a fixed velocity field, and lacked resolution overall. We aim to overcome these issues and improve previous reconstructions of the gaseous constituents of the interstellar medium of the Galaxy. We used three-dimensional (3D) Gaussian processes to model correlations in the interstellar medium, including correlations between different lines of sight, and enforce a spatially coherent structure in the prior. For modelling the transport of radiation from the emitting gas to us as observers, we took absorption effects into account. A special numerical grid ensures that there is high resolution nearby. We inferred the spatial distributions of atomic hydrogen (HI ), carbon monoxide (CO ), their emission line widths, and the Galactic velocity field in a joint Bayesian inference. We further constrained these fields with complementary data from Galactic masers and young stellar object clusters. Our main result consists of a set of samples that implicitly contain statistical uncertainties. The resulting maps are spatially coherent and reproduce the data with high fidelity. We confirm previous findings regarding the warping and flaring of the Galactic disc. A comparison with 3D dust maps reveals a good agreement on scales larger than approximately $ pc $. While our results are not free of artefacts, they present a big step forward in obtaining high-quality 3D maps of the interstellar medium.

Beyond Cohesion and Coupling: Integrating Control Flow in Software Modularization Process for Better Code Comprehensibility

As software systems evolve to meet the changing needs of users, understanding the source code becomes a critical step in the process. Clustering techniques, also known as modularization techniques, offer a solution to breaking down complex source code into smaller, more manageable parts. This facilitates improved analysis and understanding of the software’s structure. However, the effectiveness of clustering algorithms in code understanding heavily relies on the chosen criteria. While existing methods typically consider cohesion, coupling, and balance between clusters, we argue that these criteria alone may not fully satisfy one of the primary objectives of clustering, which is to enhance understanding. This is because spaghetti-like structures can be created even when these criteria are satisfied. To address this issue, we introduce two new criteria incorporating program control flow to regulate cluster dependencies. By controlling the uniformity of input and output directions, as well as the distribution of inputs and outputs, clustering algorithms can generate clusters that are more developer-friendly and easier to comprehend. We provide intuitive explanations and real-world projects to demonstrate the effectiveness of our approach, and also incorporate feedback from academics and expert programmers. This paper reveals that integrating these new criteria into existing clustering algorithms enables developers to gain deeper insights into the structure of software systems. This, in turn, leads to better design decisions and improved developer understanding of the source code.

Data Speaks: District Clustering Map to Reveal Basic Education Problems in Samarinda City

Education is an effort to create a learning environment and educational process that encourages the development of students' potential. One of the statistical methods that can be used in the field of education is cluster analysis. Average linkage is a hierarchical clustering algorithm that groups objects based on the average distance between objects in different clusters. In this study, sub-districts were grouped based on education indicators at the primary/elementary level in Samarinda City in 2023. The results of this research obtained 2 clusters that were the most optimal because they had the highest Silhouette coefficient value. Cluster 1 contains the subdistricts of Samarinda Ulu and Sungai Kunjang, while cluster 2 contains the subdistricts of North Samarinda, Palaran, Sungai Pinang, Sambutan, Loa Janan Ilir, Samarinda Ilir, Samarinda Seberang, and Samarinda Kota. Cluster 1 has an advantage over cluster 2 in several aspects of education. Cluster 1 shows a higher average in terms of the number of students, the number of classrooms, the number of institutions, the number of certified teachers, and the number of A-accredited schools at the primary/secondary level in Kota Samarinda in 2023.

INFORMATION MODEL FOR SELECTING THE OPTIMAL SPATIAL FORM OF PARTS

A complex of interrelated methods and means of analysis, clustering, segmentation and classification of objects on images obtained by sensors of different physical nature (with the help of modern methods of radiography, computer and magnetic resonance imaging) is proposed, which will allow modeling the optimal form of a bone substitute with functional properties by CAD/CAM/CAE design methods, depending on the anatomical data, the nature and localization of the cavity bone defect, its size, load conditions and patterns of bone tissue neoplasm. Methods for making multi-criteria decisions have been developed, which will allow for their implementation for a wide range of optimization tasks of researching the correlation of the shape and size of porosity with indicators of the stress-deformed state of bone substitutes in order to maintain the structural integrity of the biomaterial in the process of regeneration of hard tissue.

Divergence and Similarity Characteristics for Two Fuzzy Measures Based on Associated Probabilities

Abstract: The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions. As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC). The idea of generalization is that new definitions of distance, divergence, and similarity between fuzzy measures are reduced to the definitions of distance, divergence, and similarity between the APCs of fuzzy measures. These definitions are based on the concept of distance generator. The proof of the correctness of generalizations is provided. Constructed distance, similarity, and divergence relations can be used in such applied problems as: determining the difference between Dempster-Shafer belief structures; Constructions of collaborative filtering similarity relations; non-additive and interactive parameters of machine learning in phase space metrics definition, object clustering, classification and other tasks. In this work, a new concept is used in the fuzzy measure identification problem for a certain multi-attribute decision-making (MADM) environment. For this, a conditional optimization problem with one objective function representing the distance, divergence or similarity index is formulated. Numerical examples are discussed and a comparative analysis of the obtained results is presented.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

Weakly Supervised Underwater Object Real-time Detection Based on High-resolution Attention Class Activation Mapping and Category Hierarchy

Recently, deep learning-based underwater object detection technology has achieved remarkable success. However, the accuracy and completeness of dataset instance annotation are crucial for its success. The quality of underwater images is low, severe objects clustering, and occlusion, acquiring object's annotations demands substantial time and labor costs, while mis annotation and missed annotation can also degrade model performance and limit their application in practical scenarios. To address this issue, this paper presents a novel weakly supervised underwater object real-time detection method, which is divided into two subtasks: weakly supervised object localization and real-time object detection. In the weakly supervised object localization task, we design a novel category hierarchy structure network that integrates the high-resolution attention-class activation mapping algorithm to obtain high-quality object class activation maps, weaken background interference, and obtain more complete object regions. The parameterized spatial loss module is devised to enable the model to escape from local optimal solutions, thus accurately and efficiently obtaining object pseudo-detection annotation boxes. For the real-time object detection task, the single-stage detector YOLOv7 is selected as the basic detection model, and an object perception loss function is designed based on the class activation map to jointly supervise the training process. A method for filtering noisy pseudo-supervision information is proposed to enhance the pseudo-supervision information involved in training. Ablation experiments and multi-method comparison experiments were conducted on the URPC and RUOD datasets, and the results verify the effectiveness of the proposed strategy, and our model exhibits significant advantages in detection performance and detection efficiency compared to current mainstream and advanced models.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Mark–Scavenge: Waiting for Trash to Take Itself Out

Moving garbage collectors (GCs) typically free memory by evacuating live objects in order to reclaim contiguous memory regions. Evacuation is typically done either during tracing (scavenging), or after tracing when identification of live objects is complete (mark–evacuate). Scavenging typically requires more memory (memory for all objects to be moved), but performs less work in sparse memory areas (single pass). This makes it attractive for collecting young objects. Mark–evacuate typically requires less memory and performs less work in memory areas with dense object clusters, by focusing relocation around sparse regions, making it attractive for collecting old objects. Mark–evacuate also completes identification of live objects faster, making it attractive for concurrent GCs that can reclaim memory immediately after identification of live objects finishes (as opposed to when evacuation finishes), at the expense of more work compared to scavenging, for young objects. We propose an alternative approach for concurrent GCs to combine the benefits of scavenging with the benefits of mark–evacuate, for young objects. The approach is based on the observation that by the time young objects are relocated by a concurrent GC, they are likely to already be unreachable. By performing relocation lazily, most of the relocations in the defragmentation phase of mark–evacuate can typically be eliminated. Similar to scavenging, objects are relocated during tracing with the proposed approach. However, instead of relocating all objects that are live in the current GC cycle, it lazily relocates profitable sparse object clusters that survived from the previous GC cycle. This turns the memory headroom that concurrent GCs typically “waste” in order to safely avoid running out of memory before GC finishes, into an asset used to eliminate much of the relocation work, which constitutes a significant portion of the GC work. We call this technique mark–scavenge and implement it on-top of ZGC in OpenJDK in a collector we call MS-ZGC. We perform a performance evaluation that compares MS-ZGC against ZGC. The most striking result is (up to) 91% reduction in relocation of dead objects (depending on machine-dependent factors).

A Review of Convex Clustering From Multiple Perspectives: Models, Optimizations, Statistical Properties, Applications, and Connections.

Traditional partition-based clustering is very sensitive to the initialized centroids, which are easily stuck in the local minimum due to their nonconvex objectives. To this end, convex clustering is proposed by relaxing K -means clustering or hierarchical clustering. As an emerging and excellent clustering technology, convex clustering can solve the instability problems of partition-based clustering methods. Generally, convex clustering objective consists of the fidelity and the shrinkage terms. The fidelity term encourages the cluster centroids to estimate the observations and the shrinkage term shrinks the cluster centroids matrix so that their observations share the same cluster centroid in the same category. Regularized by the lpn -norm ( pn ∈ {1,2,+∞} ), the convex objective guarantees the global optimal solution of the cluster centroids. This survey conducts a comprehensive review of convex clustering. It starts with the convex clustering as well as its nonconvex variants and then concentrates on the optimization algorithms and the hyperparameters setting. In particular, the statistical properties, the applications, and the connections of convex clustering with other methods are reviewed and discussed thoroughly for a better understanding the convex clustering. Finally, we briefly summarize the development of convex clustering and present some potential directions for future research.

Manhattan Metric Technique in K-Means Clustering for Data Grouping

Clustering can be defined as a method commonly applied in data mining to group objects into clusters. Clusters consist of data objects that are similar to each other in a group but different from objects in other clusters. In this study, the data used is the data of KIP scholarship recipients for the 2016-2023 period. Various clustering metric measurement techniques have been frequently used by researchers, especially those focusing on distance and similarity metrics, such as Euclidean Distance, Manhattan, and Minkowski. In general, K-Means is an unsupervised learning method used in the clustering process to group data based on similarity. The elbow method is used to determine the optimal number of clusters, so that the clustering results obtained can be maximized to achieve better results. This study aims to analyze the use of Manhattan technique in K-Means clustering for data grouping. The research problem is how to analyze the Manhattan metric technique in K-Means clustering for effective data grouping. Applying the K-Means method shows that the existing data is successfully divided into four specified clusters. After determining the correct number of clusters, the K-Means method is used to sort the data in the dataset. From 3172 data, the final results obtained cluster 0 as many as 774 data, cluster 1 as many as 417 data, cluster 2 as many as 1244 data, and cluster 3 as many as 737 data. The results of the clustering process obtained a davies-bouldin index value of 1.4568.

Prototype-based contrastive substructure identification for molecular property prediction.

Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https://github.com/VRPharmer/POSIT.

Cluster Analysis of the Recent Interdecadal Variation in the Tripole Landfall Pattern of Tropical Cyclones in East Asia

Abstract Along the East Asian coast, the interdecadal variation in tropical cyclone (TC) landfall activities in the early 2010s shows a tripole change pattern characterized by significantly increased (decreased) TC landfalls to the north of 30°N and to the south of 20°N (between 20° and 30°N). The increased TC landfalls in the East Asian subregion north to 30°N mean greater TC damage in the most populous regions, including southeastern China, the Korean Peninsula, and Japan, since the early 2010s. The present work uses an objective clustering technique to analyze the internal dynamical causes of this interdecadal variation. The increased TC landfall activities in the East Asian subregion north of 30°N are attributed to the increase in TCs with a northwestward-moving track (cluster defined as C1), whereas the increased TC landfall activities to the south of 20°N and the decrease between 20° and 30°N are mainly due to the southward shift of TCs with a westward-moving track (cluster defined as C2). This work focuses on different TC track groups (C1 and C2) based on daily data, showing that the interdecadal changes in the synoptic-scale monsoon trough and subtropical high provide important environment for different groups of TCs. The ascending phase of the PDO with significant warming over the central Pacific favors the interdecadal tripole changes in TC landfalls.

Feature Selection and Dimensionality Reduction for Large Data set Clustering using Self-Organizing Maps

Data mining is a form of analyzing the data which relates the newline techniques from fields like statistics machine learning databases artificial newline intelligence etc Clustering is one of the most important data mining newline techniques in which cluster of objects are grouped based on their similarity newline "Clustering is the process of accumulating the data records into considerable newline subclasses clusters in a way which enhances the relationship within clusters newline and reduces the similarity among two different clusters. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input.

Comparison of Six Measures of Genetic Similarity of Interspecific Brassicaceae Hybrids F2 Generation and Their Parental Forms Estimated on the Basis of ISSR Markers.

Genetic similarity determines the extent to which two genotypes share common genetic material. It can be measured in various ways, such as by comparing DNA sequences, proteins, or other genetic markers. The significance of genetic similarity is multifaceted and encompasses various fields, including evolutionary biology, medicine, forensic science, animal and plant breeding, and anthropology. Genetic similarity is an important concept with wide application across different scientific disciplines. The research material included 21 rapeseed genotypes (ten interspecific Brassicaceae hybrids of F2 generation and 11 of their parental forms) and 146 alleles obtained using 21 ISSR molecular markers. In the presented study, six measures for calculating genetic similarity were compared: Euclidean, Jaccard, Kulczyński, Sokal and Michener, Nei, and Rogers. Genetic similarity values were estimated between all pairs of examined genotypes using the six measures proposed above. For each genetic similarity measure, the average, minimum, maximum values, and coefficient of variation were calculated. Correlation coefficients between the genetic similarity values obtained from each measure were determined. The obtained genetic similarity coefficients were used for the hierarchical clustering of objects using the unweighted pair group method with an arithmetic mean. A multiple regression model was written for each method, where the independent variables were the remaining methods. For each model, the coefficient of multiple determination was calculated. Genetic similarity values ranged from 0.486 to 0.993 (for the Euclidean method), from 0.157 to 0.986 (for the Jaccard method), from 0.275 to 0.993 (for the Kulczyński method), from 0.272 to 0.993 (for the Nei method), from 0.801 to 1.000 (for the Rogers method) and from 0.486 to 0.993 (for the Sokal and Michener method). The results indicate that the research material was divided into two identical groups using any of the proposed methods despite differences in the values of genetic similarity coefficients. Two of the presented measures of genetic similarity (the Sokal and Michener method and the Euclidean method) were the same.