Clustering Index Research Articles

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

RIS-Assisted Grant-Free NOMA: User Pairing, RIS Assignment, and Phase Shift Alignment

This paper introduces a reconfigurable intelligent surface (RIS)-assisted grant-free non-orthogonal multiple access (GF-NOMA) scheme. We propose a joint user equipment (UE) clustering and RIS assignment/alignment approach that jointly ensures the power reception disparity required by the power domain NOMA (PD-NOMA). The proposed approach maximizes the network sum rate by judiciously pairing UE with distinct channel gains and assigning RISs to proper clusters. To alleviate the computational complexity of the joint approach, we decouple UE clustering and RIS assignment/alignment subproblems, which reduces run times 80 times while attaining almost the same performance. Once the proposed approaches acknowledge UEs with the cluster index, UEs are allowed to access corresponding resource blocks (RBs) at any time requiring neither further grant acquisitions from the base station (BS) nor power control as all UEs are requested to transmit at the same power. In addition to passive RISs containing only passive elements and giving an 18% better performance, an active RIS structure that enhances the performance by 37% is also used to overcome the double path loss problem. The numerical results also investigate the impact of UE density, RIS deployment, RIS hardware specifications, and the fairness among the UEs in terms of bit-per-joule energy efficiency.

Evaluation of the operation status of the residential land market in Beijing-Tianjin-Hebei region of China and its spatiotemporal pattern characteristics.

Exploring the operation status and patterns of urban land markets is an important theoretical and practical topic for promoting coordinated socio-economic development. In this study, the operation status of the residential land market in the Beijing-Tianjin-Hebei region and the characteristics of its pattern were analyzed using the composite index method and the 3σ rule of the normal distribution and taking the 174 counties in Beijing, Tianjin, and Hebei, China, as the research objects. The results show that ① Beijing, Tianjin, Langfang, Zhangjiakou, and Baoding residential land market state composite indexes are all in the middle to upper levels in the Beijing-Tianjin-Hebei city cluster, while Qinhuangdao, Handan, and Chengde residential land market state composite indexes are generally low. The harmony between the residential land price and national economy, the market supply and demand balance, and the structural balance may become the main factors affecting the healthy development of the residential land market in Beijing and Tianjin. ② The proportion of counties with "healthy" residential land market in all dimensions and overall market status reached over 64%, and the residential land market in the Beijing-Tianjin-Hebei region is running well. The rapid increase in residential land prices from 2016 to 2020 was an important driver of the increased heat in the residential land market across the region. ③ The residential land market in the counties around Beijing and Tianjin is significantly hotter than in other regions, and there is an obvious polarization effect in the operation state of the residential land market in the Beijing-Tianjin region. The residential land market is generally cold in the counties in the southern and northeastern parts of the region and other peripheral areas, and there is a risk of marginalization in the development of the residential land market in the counties in the peripheral areas. ④ Both the hot and cold residential land market states exhibit spatial clustering characteristics. Most of the clusters are not consistent with the municipal administrative boundaries, and the states of the residential land market in neighboring counties are very similar.

Cord Blood as a Corrector of Hematological and Cytomorphological Characteristics of Lymphohemopoietic Organs in Experimental Atopic Dermatitis

Background. Atopic dermatitis (AD) is an inflammatory skin disease caused by a shift in the interaction between cells of the immune system and the skin. Steroid anti-inflammatory drugs used to treat AD often lead to numerous side effects. Therefore, the development of new natural multifunctional preparations with immunomodulatory activity is of great importance. Objective. To determine the changes in indices of the leukocytes cluster of peripheral blood and determine the peculiarities of cytomorphological changes in the spleen and lymph nodes (LN) of rats with induced AD before and after administration of cryopreserved (cHCBL) and lyophilized (lHCBL) forms of human cord blood leukoconcentrate. Methods. The experiments were conducted using 6-month-old Wistar rats. AD was induced by topically applying a 5% alcohol-acetone solution of dinitrochlorobenzene (DNCB) to the skin of the rats' back for 21 days. Each form of the preparation (cHCBL and lHCBL) was administered intraperitoneally in a 0.5 ml dose containing 5´106 cells, one day after the final DNCB treatment. The following parameters were assessed in the blood: leukocyte count and the neutrophil-to-lymphocyte ratio (N/L index). Сytomorphological characteristics were determined in the spleen and inguinal LN. Results. In AD, specific changes in the cytomorphological characteristics of the lymphohematopoietic complex (LHС) structures, namely the spleen and LN, were observed alongside alterations in the leukocyte cluster indices. The most notable changes in the spleen included the dominance of the white pulp over the red pulp and the prevalence of follicles of stages II and III of development in the LN. Animals with AD displayed pronounced leukocytosis and an increased N/L index in their blood. The study demonstrates the positive corrective effects of cHCBL and lHCBL on the morphological characteristics of LHC and the specified blood parameters. Conclusions. The effectiveness of the cHCBL and lHCBL use for correcting indices of leukocytes cluster of peripheral blood and the structural organization of lymphohemopoietic organs in the development of AD have been proven.

Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data

While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with six clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes Silhouette, Calinski Harabasz, and Davies Bouldin. When consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences. The proposed method combining recency, frequency, monetary value (RFM), K-means clustering, internal indices, and external indices achieved return rates of 17.50%, indicating acceptable selectivity of the customers.

Machine learning based study for the classification of Type 2 diabetes mellitus subtypes

PurposeData-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter.MethodsWe developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations –each one representing a T2DM subtype– were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset.ResultsFrom the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were 85.3% (pm 9.2%) and 97.1% (pm 3.4%), respectively. Best accuracies were 98.8% and 98.9%. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions.ConclusionThe development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.

DBH Estimation for Individual Tree: Two-Dimensional Images or Three-Dimensional Point Clouds?

Accurate forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale.

Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining

This paper proposes a spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation between data points and improve the clustering accuracy of Laplacian matrix eigenvectors. First, we used the correlation coefficient as the weight of the Mahalanobis distance to calculate the weighted Mahalanobis distance between any two data points and constructed the weighted Mahalanobis distance matrix of the data set; then, based on the weighted Mahalanobis distance matrix, we used the K-nearest neighborhood (KNN) algorithm construct similarity matrix. Secondly, the regularized Laplacian matrix was calculated according to the similarity matrix, normalized and decomposed, and the feature space for clustering was obtained. This method fully considered the degree of linear correlation between data and special spatial structure and achieved accurate clustering. Finally, various spectral clustering algorithms were used to conduct multi-angle comparative experiments on artificial and UCI data sets. The experimental results show that MDLSC has certain advantages in each clustering index and the clustering quality is better. The distribution results of the eigenvectors also show that the similarity matrix calculated by MDLSC is more reasonable, and the calculation of the eigenvectors of the Laplacian matrix maximizes the retention of the distribution characteristics of the original data, thereby improving the accuracy of the clustering algorithm.

Improvement of DBSCAN Algorithm Based on K-Dist Graph for Adaptive Determining Parameters

For the shortcomings of an unstable clustering effect and low accuracy caused by the manual setting of the two parameters Eps and MinPts of the DBSCAN (density-based spatial clustering of applications with noise) algorithm, this paper proposes an adaptive determination method for DBSCAN algorithm parameters based on the K-dist graph, noted as X-DBSCAN. The algorithm uses the least squares polynomial curve fitting method to fit the curve in the K-dist graph to generate a list of candidate Eps parameters and uses the mathematical expectation method and noise reduction threshold to generate the corresponding MinPts parameter list. According to the clustering results of each group of parameters in the Eps and MinPts parameter lists, a stable range of cluster number changes is found, and the MinPts and Eps corresponding to the maximum K value in the stable range are selected as the optimal algorithm parameters. The optimality of this parameter was verified using silhouette coefficients. A variety of experiments were designed from multiple angles on the artificial dataset and the UCI real dataset. The experimental results show that the clustering accuracy of X-DBSCAN was 21.83% and 15.52% higher than that of DBSCAN on the artificial and real datasets, respectively. The X-DBSCAN algorithm was also superior to other algorithms through comprehensive evaluation and analysis of various clustering indicators. In addition, experiments on four synthetic Gaussian datasets of different dimensions showed that the average clustering indices of the proposed algorithm were above 0.999. The X-DBSCAN algorithm can select parameters adaptively in combination with the characteristics of the dataset; the clustering effect is better, and clustering process automation is realized.

Two Medoid-Based Algorithms for Clustering Sets

This paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous medical history. The first proposed algorithm is based on K-medoids and the second algorithm extends the random swap algorithm, which has proven to be capable of efficient and careful clustering; both algorithms depend on a distance function among data objects (sets), which can use application-sensitive weights or priorities. The proposed distance function makes it possible to exploit several seeding methods that can improve clustering accuracy. A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the O(N2) computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for the handling of non-trivial datasets. This paper applies the algorithms to several benchmark case studies of sets and demonstrates how accurate and time-efficient clustering solutions can be achieved.

Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa

IntroductionSignificant regional variations in the HIV epidemic hurt effective common interventions in sub-Saharan Africa. It is crucial to analyze HIV positivity distributions within clusters and assess the homogeneity of countries. We aim at identifying clusters of countries based on socio-behavioural predictors of HIV for screening.MethodWe used an agglomerative hierarchical, unsupervised machine learning, approach for clustering to analyse data for 146,733 male and 155,622 female respondents from 13 sub-Saharan African countries with 20 and 26 features, respectively, using Population-based HIV Impact Assessment (PHIA) data from the survey years 2015–2019. We employed agglomerative hierarchical clustering and optimal silhouette index criterion to identify clusters of countries based on the similarity of socio-behavioural characteristics. We analyse the distribution of HIV positivity with socio-behavioural predictors of HIV within each cluster.ResultsTwo principal components were obtained, with the first describing 62.3% and 70.1% and the second explaining 18.3% and 20.6% variance of the total socio-behavioural variation in females and males, respectively. Two clusters per sex were identified, and the most predictor features in both sexes were: relationship with family head, enrolled in school, circumcision status for males, delayed pregnancy, work for payment in last 12 months, Urban area indicator, known HIV status and delayed pregnancy. The HIV positivity distribution with these variables was significant within each cluster.Conclusions /findingsThe findings provide a potential use of unsupervised machine learning approaches for substantially identifying clustered countries based on the underlying socio-behavioural characteristics.

Green development evaluation of China’s Yangtze River Economic Belt based on hierarchical clustering and composite ecosystem index system

Green development evaluation of China’s Yangtze River Economic Belt based on hierarchical clustering and composite ecosystem index system

Linguistic clustering and aggregate productive efficiency in Indonesia

Purpose The purpose of this study is to explore the link between aggregate production efficiency and the extent of linguistic clustering in Indonesia. Design/methodology/approach The author draws on the stochastic frontier model and applies it to the data on Indonesian provinces to compute the effects of various determinants on these provinces' aggregate production efficiency. The key determinant is the spatial index of linguistic clustering that the author believes has never been applied before in this context. Findings Linguistic clustering is an important determinant of aggregate production efficiency. Linguistic diversity is positively associated with productive efficiency if members of a specific linguistic group are not clustered beyond a certain level. Originality/value To the best of the author’s knowledge, this is the first study that links the spatial index of linguistic clustering (because of Massey and Danton) to production efficiency. In other words, the contribution of this study is to introduce a geographical dimension to the mainstream analysis of the association between ethnic diversity and economic performance.

COMPARISON OF K-MEANS AND GAUSSIAN MIXTURE MODEL IN PROFILING AREAS BY POVERTY INDICATORS

The Covid-19 pandemic has led to income degradation of the Indonesia population which potentially triggers poverty. According to the Indonesian Central Statistics Agency, the Province of Central Java is one of the areas that is most affected by Covid-19 especially on the economic aspect. In 2020, the percentage of poor people has increased by 0.6% from 2019. If this condition is ignored for the long term, it will have a negative impact on hampering national development. As a first step in designing a strategy for mitigating the impact of poverty, it is necessary to carry out an appropriate profiling of the areas affected on the economic aspect based on poverty indicators. This study compares the K-Means Clustering and Gaussian Mixture Model (GMM) in providing the best data grouping based on clustering indexes, including: connectivity, Dunn, and silhouette. GMM is a generalization of K-Means clustering to include information about the covariance structure of the data as well as latent Gaussian centers. We used poverty indicators data from Central Statistics Agency of Central Java, such as poverty line, percentage of poor population, poverty depth index, and poverty severity index. The results obtained from this study indicate that the GMM gives the best results with the 3 clusters, with the number of members for the first, second, third is 10, 19, and 6 respectively.

Study of the Group Vibrational Detachment Characteristics of Litchi (Litchi chinensis Sonn) Clusters

A high-speed camera was applied to observe the shaking detachment tests with ‘Guiwei’ litchi clusters using 7 Hz, 9.5 Hz, and 12 Hz and the amplitudes of 40 mm, 60 mm, and 80 mm. The fruit’s detachment time and the number of fruit–fruit impacts were affected by the vibrational parameters and the fruit position (inside or outside of the cluster). A high and stable detachment speed and a low number of impacts could be achieved with the parameter combination of 9.5 Hz/80 mm. The cluster index, considering both the shaking position and the total fruit mass, was found to have a stronger correlation with the detachment time than the average fruit-to-shaker distance (distance from the shaker to the fruit). Thus, the detachment time can be estimated based on the cluster indexes. The fruit-to-shaker distance also affected the fruit-to-shaker impact. A fruit-to-shaker distance of 50~60 mm was recommended to avoid almost all the impacts, depending on the shaking parameters. The horizontal throw distance of the fruit was calculated based on the detachment velocity and angle, which can be used as the design basis for the catching devices. With a high enough drop height, a disc-shaped catching surface with a radius of 475 mm, 489 mm, 591 mm, 741 mm, 844 mm, and 832 mm, respectively, could collect 90% of all the fruit at six different vibrational parameter combinations. A closer catching surface to the fruit could effectively reduce the horizontal throw distance.

APPLYING DEEP LEARNING FOR HEALTHCARE IN SMART CITY VIA INTERNET OF THINGS

In the traditional city for healthcare in IoT, it has been proposed to replace traditional yield models with mathematical models that do not require the assumption of defect density functions. The selection of input parameters in these models is very important, and all the variation factors on the wafer must be included as far as possible. The factors of clustering are usually described by clustering indicators, but some specific clustering patterns will cause the clustering indicators to misjudge the clustering degree, resulting in the yield estimation error becoming larger. In view of this, the proposed study has classified the defect patterns on the wafer into four types: random distribution, regional concentrated distribution, linear distribution and circular distribution, by means of three pattern characteristics analysis. A comparison is made only using cluster indicators to describe cluster phenomena and a model that uses cluster indicators and cluster graphs to describe cluster phenomena. The research results show that when constructing the yield model, the clustering pattern and the clustering index are used to describe the clustering phenomenon in smart city via Internet of things, which is preferred to solely considering the clustering index, as the consequent degree of accuracy far exceeds the improvement of changing the “number of effective grains” in relation to the clustering index. Therefore, the yield rate can be estimated more accurately by using clustering graphs with clustering indicators; the estimated yield in the yield model, with the clustering pattern parameter, is indeed closer to the actual yield than the yield model without the clustering pattern parameter.

Low-voltage series arc fault detection based on ECMC and VB-SCN

The currents of low-voltage series arc faults are non-stationary and nonlinear, so the selection of fault features and the adaptability of the detection algorithm will affect the detection results. In response to these issues, firstly, a feature selection method combining Euclidean distance, classifier criterion, max-relevance min-redundancy and clustering index is proposed, ECMC. This method can filter out weak correlation features and redundant information in the time, frequency and time–frequency domains. The optimal feature data set constructed by ECMC can fully reflect the characteristics of arc faults. Then, a stochastic configuration network (SCN) based on variational Bayesian (VB) optimization, VB-SCN, is proposed. This method uses VB to optimize the output weight and scale function of SCN iteratively. VB-SCN can improve the quality of hidden-layer nodes and the generalization ability of the network while ensuring the adaptive learning of current features, making arc fault detection fast and stable. Finally, the experiments based on the self-built sample set prove that the optimal feature dataset constructed by ECMC has perfect separability. VB-SCN can adaptively realize the accurate and fast detection of arc faults. The detection accuracy is stable between 98.2506% and 100%, which has better robustness and stability.

Cluster analysis for the selection of potential discriminatory variables and the identification of subgroups in archaeometry

In this article, three variable selection methods based on Gaussian mixture models were compared to find a subset of variables that provided the “best” clustering. The use of an appropriate transformation for compositional data, whose geometric space is the Simplex, is emphasized. The comparison revealed the ability of the models to cluster data in multiple phases, showing to be more convenient to select the relevant variables than to perform an analysis based on 2D plots or by simultaneously including all the available variables in a multivariate analysis. Once the informative variables for the clustering were obtained, we used a method called Databionic Swarm (DBS). This method uses unsupervised machine learning, taking advantage of emergence and swarm intelligence applied to find natural chemical groups in the input data space. DBS can visualize high-dimensional distances in the projection through a 3D topographic map with hypsometric tints. The results were compared in terms of accuracy, both in the selection of the variables and in the classification, using a supervised accuracy index for clustering and two unsupervised indexes (the Heatmap and the Silhouette plot). The concepts and methods were illustrated by applying them to two published archaeological glass data sets. The first set consisted of 245 Romano-British glass vessels and the second set of 180 glass vessels from the 15th-17th century in Antwerp. In these applications, it was found that the methods for the selection of variables increased the accuracy of the classification compared to traditional methods.

Encoding of everyday objects in older adults: Episodic memory assessment in virtual reality.

Age-related decline in episodic memory performance in otherwise healthy older adults is indisputably evident. Yet, it has been shown that under certain conditions episodic memory performance in healthy older adults' barely deviates from those seen in young adults. Here we report on the quality of object encoding in an ecologically valid, virtual-reality based memory assessment in a sample of healthy older and younger adults with comparable memory performance. We analyzed encoding by establishing both a serial and semantic clustering index and an object memory association network. As expected, semantic clustering was superior in older adults without need for additional allocation of executive resources whereas young adults tended more to rely on serial strategies. The association networks suggested a plethora of obvious but also less obvious memory organization principles, some of which indicated converging approaches between the groups as suggested by a subgraph analysis and some of which indicated diverging approaches as suggested by the respective network interconnectivity. A higher interconnectivity was observed in the older adults' association networks. We interpreted this as a consequence of superior semantic memory organization (extent to which effective semantic strategies diverged within the group). In conclusion, these results might indicate a diminished need for compensatory cognitive effort in healthy older adults when encoding and recalling everyday objects under ecologically valid conditions. Due to an enhanced and multimodal encoding model, superior crystallized abilities might be sufficient to counteract an age-related decline in various other and specific cognitive domains. This approach might potentially elucidate age-related changes in memory performance in both healthy and pathological aging.

Clusterization of Ukraine regions according to the integrated index of ecological and economic security in the destructive phenomena conditions

Destructive phenomena conditions, such as the COVID-19 pandemic in 2020, hostilities in Eastern Ukraine and the annexation of the Autonomous Republic of Crimea in 2014, and the Global Financial Crisis in 2008, have significantly affected Ukraine’s environmental and economic security. The self-development processes of Ukraine’s safety began to slow down, which led to the degradation of the interconnection system between the environment and the economy. The study aims to determine the destructive phenomena’ impact on the environmental and economic security level of Ukraine using an improved method of normalised clustering indices to analyse the distribution of Ukraine’s regions into homogeneous groups at the level of integrated environmental and economic security. This index is calculated based on stimulating and disincentive normative indicators from 2004 to 2020. With the help of clustering of regions, the adverse effects of destructive phenomena on individual parts and their groups were identified. The obtained results make it possible to track the positive and negative processes of state intervention in the mechanism to ensure the level of environmental and economic security.