Partitional Clustering Methods Research Articles

Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller.

Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra

The k-means algorithm is a partitional clustering method. K-Means has several advantages, including being easy to implement, having a high level of convergence and producing denser clusters. Meanwhile, the drawback is that it is difficult to determine the optimal number of clusters. The K-Means method will be used to solve problems in areas prone to flood disasters in North Sumatra. This research aims to find the optimal number of clusters with the Calinski Harabasz Index and Krzanowski And Lai Index based on the Cluster Tightness Measure (CTM) value. There are eleven variables used in this research. Based on the research results, it was concluded that the CTM CH result of 0.376 was smaller than the CTM KL of 0.7843. So it can be said that determining the optimal number of clusters using CH with k = 6 is better than KL with k = 2.

Generalized black hole clustering algorithm

The Black Hole Clustering (BHC) algorithm is a density-based partitional clustering method inspired by the Density-based Spatial Clustering of Applications with Noise (DBSCAN). It does not require the number of clusters nor the computation of the pair-wise distance matrix between the data points, making it faster than DBSCAN. Also, it only needs one parameter that is intuitively easier to set than the epsilon parameter of DBSCAN. However, BHC needs the allocation of the so-called black holes that have to be linearly independent, making the algorithm in its current version suitable only for two or three-dimensional data sets. In this paper, we propose a generalized version of the black hole clustering algorithm (GBHC) by introducing a novel black hole allocation procedure for higher-dimensional data spaces. Furthermore, the proposed method is data-independent, so we have to run it once to obtain the black hole positions for all finite-dimensional metric spaces. We performed extensive computational experiments to compare GBHC with DBSCAN. The results show that both algorithms obtain comparable clustering solutions. GBHC, however, outperforms DBSCAN in computational complexity and explainability.

Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids

<p><em>The territory of Indonesia is located in geographical, geological, hydrological, and demographic conditions that allow Indonesia to be prone to disasters. The most common natural disaster in Indonesia is flooding. If accumulated, there have been 682 flood events in the country since the beginning of 2022. In Indonesia, especially West Java Province, flooding is the most common disaster, especially during the rainy season. So a study will be conducted that aims to determine the grouping of districts / cities in West Java Province based on the occurrence of flood disasters. The data used in this study were obtained from the publication of the National Disaster Management Agency. In this study, there are 4 variables of the impact of flood disasters, namely total deaths, total submerged houses, total damaged houses and total injured. The clustering method used in this research is K-Medoids. K-Medoids is one of the clustering methods that uses the partition clustering method in grouping a set of n objects into a number of k clusters. From the results of the K-Medoids analysis, three clusters were obtained. The first cluster consists of 3 districts/cities with high impact of flood disasters, the second cluster consists of 23 districts/cities with moderate impact of flood disasters, and the third cluster consists of 1 district/city with low impact of flood disasters. Based on the results of the analysis, efforts can be made by the government to focus more on designing steps that must be taken in preventing or overcoming the impact of flood disasters.</em></p><p><strong><em>Keywords: </em></strong><em>Cluster; K-Medoids; Floods West Java</em></p>

Capacitated spatial clustering with multiple constraints and attributes

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.

ANALISIS k-MEDOIDS DENGAN VALIDASI INDEKS PADA IPM DAERAH 3T DI INDONESIA

Human development is a development paradigm that places humans as the main target of all development activities, namely controling over resources, improving health and improving education. The Human Development Index (HDI) in Indonesia varies in each district, especially in the 3T areas. The 3T area is an area that is classified as underdeveloped, remote and outermost in terms of economy, health, education and infrastructure. The k-Medoids method is a partitional clustering method for grouping several objects into clusters. This clustering algorithm uses the medoid as the center of the cluster, so it is robust to data containing outliers. This study aims to classify the 3T regions in Indonesia based on the Human Development Index to find out which areas require more attention from the government in optimizing the Human Development Index numbers. The size of object similarity is calculated by using the Euclidean distance and Manhattan distance, for the selection of the best number of clusters, internal cluster validation, such as Calinski – Harabasz index, Gamma Index, and Silhouette index. The result of this study showed that the best cluster were four by using Euclidean distance measurement, having Calinski – Harabasz index score of 37.15764, Gamma index score of 0.7821181, and Silhouette index score of 0.3354435.

Comparative analysis of ANFIS models in Prediction of Streamflow: the case of Seyhan Basin

In order to sustain human life without problems, a rational planning is required for the conservation and use of existing water resources. The potential of future water sources should be determined as the first step in such planning. Therefore, river flow forecasting is necessary to provide basic information about a variety of problems related to the operation of river systems. In this study, the long-term daily flow values of the Zamantı River-Değirmenocağı, Zamantı River-Ergenuşağı, and Eğlence River-Eğribük stations in the Seyhan Basin in Turkey were examined. In order to predict the forward flow rate from past flow measurement values, the Adaptative Neuro-Fuzzy Inference System (ANFIS) model was trained using Backpropagation (BP), Hybrid Learning (HB), and Simulated Annealing (SA) algorithms, and the results were compared. The performance of ANFIS models created with different input parameters using Grid Partitioning (GP) and Fuzzy C-Means Clustering (FCM) methods was also examined. The evaluation criteria used for comparison were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Determination Coefficient (R2), and Mean Absolute Percentage Error (MAPE). The best results for R2 values of 0.6854, 0.9242, and 0.9373 were obtained for FMSs using the BP model. As a result of the analysis, it was concluded that the BP algorithm could be used more successfully and effectively than other algorithms for training ANFIS parameters in nonlinear problems.

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

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

A survey on the utilization of Superpixel image for clustering based image segmentation.

Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.

Robust two-layer partition clustering of sparse multivariate functional data

A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this proposed distance, the new approach not only can detect correct clusters for sparse multivariate functional data under outlier settings but also can detect those outliers that do not belong to any clusters. Classical distance-based clustering methods such as density-based spatial clustering of applications with noise (DBSCAN), agglomerative hierarchical clustering, and K-medoids are extended to the sparse multivariate functional case based on the newly-proposed distance. Numerical experiments on simulated data highlight that the performance of the proposed algorithm is superior to the performances of existing model-based and extended distance-based methods. The effectiveness of the proposed approach is demonstrated using Northwest Pacific cyclone tracks data as an example.

Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.

Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

OMCOKE: A Machine Learning Outlier-based Overlapping Clustering Technique for Multi-Label Data Analysis

Clustering is one of the challenging machine learning techniques due to its unsupervised learning nature. While many clustering algorithms constrain objects to single clusters, K-means overlapping partitioning clustering methods assign objects to multiple clusters by relaxing the constraints and allowing objects to belong to more than one cluster to better fit hidden structures in the data. However, when datasets contain outliers, they can significantly influence the mean distance of the data objects to their respective clusters, which is a drawback. Therefore, most researchers address this problem by simply removing the outliers. This can be problematic especially in applications such as fraud detection or cybersecurity attacks risk analysis. In this study, an alternative solution to this problem is proposed that captures outliers and stores them on-the-fly within a new cluster, instead of discarding. The new algorithm is named Outlier-based Multi-Cluster Overlapping K-Means Extension (OMCOKE). Empirical results on real-life multi-label datasets were derived to compare OMCOKE’s performance with other common overlapping clustering techniques. The results show that OMCOKE produced a better precision rate compared to the considered clustering algorithms. This method can benefit various stakeholders as these outliers could have real-life applications in cybersecurity, fraud detection, and the anti-phishing of websites.

Analysis of k-medoids clustering on toddler immunization in north sumatra province

Immunization is a process to increase the body's immune system by inserting a vaccine, namely a virus or bacteria that has been weakened, killed, or parts of the bacteria (virus) have been modified. The purpose of this study was to group children's immunization data according to districts/cities in North Sumatra Province using the K-Medoids Data Mining algorithm. The K-Medoids algorithm or pattern known as PAM (Partitioning Around Medoids) uses the partitioning clustering method to group a set of n objects into a number of K clusters. The data in this study is sourced from the Central Bureau of Statistics of North Sumatra. The grouping was carried out based on the number of recipients of the DPT-HB and Measles/MR vaccines from 33 districts/cities in North Sumatra Province. The results of the study are expected to be taken into consideration for local governments, especially the Ministry of Health in the distribution of immunization for children under five with the type of immunization to achieve the national target set by the Ministry of Health, which is 79.1%.

Data clustering using leaders and followers optimization and differential evolution

Data clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficulties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.

An automatic feature extraction technique from the images of granular parakeratosis disease

The largest and most vital part of the human body is skin and any change in the features of skin is termed as a skin lesion. The paper considers granular parakeratosis lesion that is an epidermal reaction occurring due to the disorder of keratinization, and mainly seen in intertriginous areas. The manual inspection of the lesion features is a bit cumbersome due to which an automated system is proposed in this paper. The main goal is to determine the size and depth of granular parakeratosis lesions using the proposed ensemble algorithm, partition clustering and region properties method. As a flow of the proposed model, segmentation is done using U-net with binary cross entropy, features are extracted using partition clustering and region properties method, and classification is done using SVM 10-fold model. The proposed feature extraction method estimates the depth and absolute size of K lesions in each image by predicting the absolute height and width of the lesion in terms of pixel square. After extracting the features, classification is done, thereby obtaining an accuracy of 95%, sensitivity and specificity of 100%. The proposed model provides better performance compared to state-of-the-art models. The main application of this automated system is in dermatology field where some skin lesions have same features which makes the experts to diagnose the disease incorrectly. If the proposed system is incorporated, diagnosis can be done in an effective manner considering all the relevant features.

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

Clustering is a technique in data mining that groups a set of data into groups (clusters) of similar data. In general, there are two methods of clustering, namely the hierarchical method and the partition method. One of the most commonly used partition clustering methods in clustering is K-Means. The use of K-means method has been widely used in various fields with various purposes. Many research has been carried out to improve the performance of the K-Means method, for example, by modifying the method of determining the initial centroid or determining the appropriate number of clusters. In this research, the modification of the K-Means algorithm was carried out in calculating the distance by considering the correlation value between attributes. Attributes that have a high correlation value are assumed to have similar characteristics so that they determine the location of data in a particular cluster. The steps of the proposed method are: calculating the correlation value between attributes, determining the cluster centroid, calculating the distance by considering the value of correlation, and determining the data into certain clusters. The first contribution of this research is to propose a new distance calculation technique in the K-Means algorithm by considering correlation and the second contribution is to apply the proposed algorithm to a specific dataset, namely Iris dataset. In this research, the performance calculation of the modified algorithm was also carried out. From the experimental results using the Iris dataset, the proposed modification of the K-Means algorithm has fewer iterations than the original K-Means method, so that it requires less processing time. The original K-Means method requires 8 iterations, while the proposed method requires only 6 iterations. The proposed method also produces a higher accuracy rate of 89.33% than the original K-Means method, which is 82.67%.

Seafood freshness: e-nose data for classification purposes

The aim of this study was to develop an electronic nose system useful for the inspection of different seafood products and specifically designed to be applied as an auxiliary device for the management of seafood in the large-scale distribution chain. Since the freshness is a critical issue in determining the quality of fish it is of great importance to have a simple and rapid analytical method representing a useful tool for assessing the acceptability of the seafood products in the distribution centers or in the supermarkets. In this work a portable e-nose, composed by four selected metal oxide semiconductor sensors, a photoionization detector and two electrochemical cells, was applied to test three different seafood products - sole (Solea senegalensis) fillets, red mullet (Mullus barbatus) fillets and cuttlefish (Seppia officinalis) - during their shelf life from the day of arrival and packaging at the distribution center until beyond the expiration date. The K-means partitional clustering method was applied to group samples into three freshness classes, then confirmed by microbial analysis; two classification models based on k-nearest neighbors (K-NN) and partial least square-discriminant analysis were developed to classify the freshness of the seafood regardless the species and in particular, the KNN model provided 100% overall sensitivity, specificity and precision in prediction thus confirming the applicability of the system to inform retailers on product safety and quality.

Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach

Cardiovascular disease is one of the most common diseases in the modern world, which, if diagnosed early, can greatly reduce the damage to the patient. Diagnosis of heart disease requires great care, and in some cases, the process can be disrupted by human error. Machine learning methods, especially data mining, have gained international acceptance in almost all aspects of life, especially the prediction of heart disease. On the other hand, datasets related to heart patients have many biological features that most of these features do not have a direct impact on diagnosis. By removing redundant features from the dataset, in addition to reducing computational complexity, the accuracy of heart patients’ predictions can also be increased. This paper presents a density-based unsupervised approach to the diagnosis of abnormalities in heart patients. In this method, the basic features in the dataset are first selected based on the filter-based feature selection approach. Then, the DBSCAN clustering method with adaptive parameters has used to increase the clustering accuracy of healthy instances and to determine abnormal instances as cardiac patients. Partition clustering methods suffer from the selection of the number of clusters and the initial central points and are very sensitive to noise. The DBSCAN method solves these problems by creating density-based clusters, but the selection of the neighborhood radius threshold and the number of connected points in the neighborhood remains unresolved. In the proposed method, these two parameters are selected adaptively to achieve the highest accuracy for the diagnosis and prediction of heart patients. The results of the experiments show that the accuracy of the proposed method for predicting heart patients is approximately 95%, which has improved in comparison with previous methods.

Finite Mixture of Birnbaum–Saunders Distributions Using the k-Bumps Algorithm

Mixture models have received a great deal of attention in statistics due to the wide range of applications found in recent years. This paper discusses a finite mixture model of Birnbaum–Saunders distributions with G components, which is an important supplement to that developed by Balakrishnan et al. (J Stat Plann Infer 141:2175–2190, 2011) who considered a model with two components. Our proposal enables the modeling of proper multimodal scenarios with greater flexibility for a model with two or more components, where a partitional clustering method, named k-bumps, is used as an initialization strategy in the proposed EM algorithm to the maximum likelihood estimates of the mixture parameters. Moreover, the empirical information matrix is derived analytically to account for standard error, and bootstrap procedures for testing hypotheses about the number of components in the mixture are implemented. Finally, we perform simulation studies to evaluate the results and analyze two real dataset to illustrate the usefulness of the proposed method.