Classical Clustering Methods Research Articles

Integrating agent-based models and clustering methods for improving image segmentation

Image segmentation through clustering is a widely used technique in computer vision that partitions an image into multiple segments by grouping pixels based on feature similarity. Although effective for certain applications, this approach often struggles with the complexity of real-world images, where noise and random variations can significantly affect feature homogeneity, leading to incorrect pixel classifications. To address these limitations, this paper introduces a novel hybrid image segmentation method that combines an agent-based model with a clustering technique to enhance segmentation accuracy and robustness. The method starts with an agent-based model as a preprocessing step aimed at homogenizing pixel intensities within each region. In this model, pixels adjust their intensities based on a consensus reached within their neighborhood, promoting a more uniform feature distribution. Subsequently, the Firefly metaheuristic clustering method is applied to segment the preprocessed image into distinct regions. Metaheuristic techniques, distinguished from classical clustering methods, possess the capability to adaptively navigate through a broad solution space to discover optimal clustering configurations. This adaptability makes them suitable for complex image datasets. The efficacy of the proposed hybrid segmentation method has been tested on various images, employing key quality indices for evaluation. Experimental outcomes demonstrate that this approach yields superior segmented images, showcasing enhanced quality and robustness compared to other segmentation methods.

Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers

Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This paper introduces a provably robust clustering algorithm based on loss minimization that performs well on Gaussian mixture models with outliers. It provides theoretical guarantees that the algorithm obtains high accuracy with high probability under certain assumptions. Moreover, it can also be used as an initialization strategy for k-means clustering. Experiments on real-world large-scale datasets demonstrate the effectiveness of the algorithm when clustering a large number of clusters, and a k-means algorithm initialized by the algorithm outperforms many of the classic clustering methods in both speed and accuracy, while scaling well to large datasets such as ImageNet.

Clustering of Electric Power Systems into Reliability Zones in Adequacy Assessment. Part 2

The article presents an analysis of clustering methods in relation to the problem of the formation of energy calculation models (ECM) of electric power systems (EPS). Classical clustering methods are considered, such as: the k-means method, the shortest path method, as well as methods for detecting communities in graphs according to given features, which are the most suitable for solving the problem. Based on the analysis, it was found that for graph clustering, the most adequate result can be obtained by applying the Leiden method. The experimental part of the article presents the results of applying the Leiden method for the formation of the ECM of the unified energy system of Siberia.

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms. By comparing classical clustering methods (k-means, DBSCAN, and SR-Tesseler) with deep-learning-based approaches (YOLO, DETR, Deformable DETR, and Faster R–CNN) for the analysis of super-resolution fluorescence images of exosomes, we identified the deep-learning algorithm, Deformable DETR, as the most effective. It showed superior accuracy and a reduced processing time for detecting individual vesicles from SRM images. Our findings demonstrate that image-based deep-learning-enhanced methods from SRM images significantly outperform traditional coordinate-based clustering techniques in identifying individual vesicles and resolving the challenges related to misidentification and computational demands. Moreover, the application of the combined Deformable DETR and ConvNeXt-S algorithms to differently labeled exosomes revealed its capability to differentiate between them, indicating its potential to dissect the heterogeneity of vesicle populations. This breakthrough in vesicle analysis suggests a paradigm shift towards the integration of AI into super-resolution imaging, which is promising for unlocking new frontiers in vesicle biology, disease diagnostics, and the development of vesicle-based therapeutics.

A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named K2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${K}^{2}$$\\end{document}-nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

Grouping Districts / Cities in Central Sulawesi Province Based on Poverty Indicators Using the Fuzzy Geographically Weighted Clustering -Artificial Bee Colony Method

Introduction: Poverty is the main problem that is the focus of attention of the government in Indonesia. In general, poverty is a person's inability to meet basic basic needs in every aspect of life. Cluster analysis is a solution to map this problem. Method: Fuzzy Geographically Weighted Clustering-Artificial Bee Colony (FGWC-ABC) is one clustering method that is an integration of classical fuzzy clustering methods and geodemographic elements. Artificial Bee Colony is a metaheuristic algorithm that is used as a global optimization to increase cluster accuracy. Artificial Bee Colony can efficiently and effectively solve various function optimization problems in various cases. Result and Discussion: The research results obtained 3 optimum clusters with each cluster characteristic relatively different based on poverty indicators. Cluster 1 with low poverty, cluster 2 with high poverty, and cluster 3 with moderate poverty. Conclusion: By using the IFV validity index, 3 optimum clusters were obtained with different characteristics of each cluster based on its indicators. Cluster 1 consists of three regencies/cities with low poverty status, cluster 2 consists of seven regencies/cities with high poverty status, and cluster 3 consists of six regencies/cities with moderate poverty status.

A New Approach In Metaheuristic Clustering: Coot Clustering

As a result of technological advancements, the increase in vast amounts of data in today's world has made artificial intelligence and data mining significantly crucial. In this context, the clustering process, which aims to explore hidden patterns and meaningful relationships within complex datasets by grouping similar features to conduct more effective analyses, holds vital importance. As an alternative to classical clustering methods that face challenges such as large volumes of data and computational complexities, a metaheuristic clustering method utilizing Coot Optimization (COOT), a swarm intelligence-based algorithm, has been proposed. COOT, inspired by the hunting stages of eagles and recently introduced into the literature, is a metaheuristic method. Through the proposed COOT metaheuristic clustering method, the aim is to contribute to the literature by leveraging COOT's robust exploration and exploitation processes, utilizing its dynamic and flexible structure. Comprehensive experimental clustering studies were conducted to evaluate the consistency and effectiveness of the COOT-based algorithm using randomly generated synthetic data and the widely used Iris dataset in the literature. The same datasets underwent analysis using the traditional clustering algorithm K-Means, renowned for its simplicity and computational speed, for comparative purposes. The performance of the algorithms was assessed using cluster validity measures such as Silhouette Global, Davies-Bouldin, Krznowski-Lai, and Calinski-Harabasz indices, along with the Total Squared Error (SSE) objective function. Experimental results indicate that the proposed algorithm performs clustering at a competitive level with K-Means and shows potential, especially in multidimensional datasets and real-world problems. Despite not being previously used for clustering purposes, the impressive performance of COOT in some tests compared to the K-Means algorithm showcases its success and potential to pioneer different studies aimed at expanding its usage in the clustering domain.

Estimation curve of multivariate adaptive biresponse fuzzy clustering means regression splines approach to stunting and wasting cases in Southeast Sulawesi

This article offers a new method of clustering data. This method is constructed by combining the multivariate adaptive regression spline biresponse continuous model (MARSBC) with the fuzzy clustering means (FCM) approach, called the multivariate adaptive biresponse fuzzy clustering means regression splines (MABFCMRS) model. This method uses patterns obtained from the MARSBC model to separate data into specific groups. Observing unobserved heterogeneity that has not been obtained from previous models. Unlike the classic fuzzy clustering methods that use euclid distances to determine the weight of the object, this method uses the total square of the massed residual distance generated by the MARSBC model. Theoretical studies were conducted to obtain predictions for the MABFCMRS model parameters. Furthermore, this method was applied to stunting and wasting cases in southeastern Sulawesi province. The results of the research show that in the case of stunting modeling and wasting in southeast Sulawesi province, the best clusters were obtained based on the criteria of partition coefficient (PC) and modification of PC (MPC). This research is able to show that the clustering process using the MABFCMRS model has been able to improve generalized cross-validation (GCV) values and determination coefficients.•This paper presents a new, effective method for clustering data based on unobserved heterogeneity.•This is applicable to sizable samples with 3 to 20 predictors.

A Comprehensive Survey on Community Detection With Deep Learning.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Deep clustering framework review using multicriteria evaluation

The application of clustering has always been an important method for problem-solving. In the era of big data, most classical clustering methods suffer from the curse of dimensionality and scalability issues. Recently, deep clustering models have garnered more attention due to their capabilities in dealing with complex, high-dimensional, and large-scale datasets. They offer intriguing perspectives owing to their outstanding representative capacity and fast inference speed. The remaining major problem in clustering scenarios with high-dimensional data revolves around determining an appropriately compressed representation that semantically preserves cluster structures. Without labels, defining an objective function to encourage a suitable representation becomes a critical question. After several years of stagnation, impressive results have been achieved in the last two years. This paper proposes a comprehensive and up-to-date review of deep clustering methods. We first introduce the basic concepts shared by several deep clustering algorithms, available network architectures, and optimization strategies. Then, a detailed review is presented for each family by analyzing their most representative algorithms. These algorithms are then assessed based on their classification accuracy and from a multi-criteria perspective to aid investigators in selecting the most appropriate solution. Finally, an overview of the diversity of tasks and application domains is provided, and current issues and challenges are discussed.

Unsupervised Deep Feature Learning for Icequake Discrimination at Neumayer Station, Antarctica

Abstract Unsupervised machine learning methods are gaining attention in the seismological community as more and larger datasets of continuous waveforms are collected. Recently, contrastive learning for unsupervised feature learning has shown great success in the field of computer vision and other domains, and we aim to transfer these methods to the domain of seismology. Contrastive learning algorithms use data augmentation to implement an instance-level discrimination task: The feature representations of two augmented versions of the same data example are trained to be similar, when at the same time dissimilar to other data examples. In particular, we use the popular contrastive learning method SimCLR. We test data augmentation strategies varying amplitude and frequency of seismological signals, and apply contrastive learning methods to automatically learn features. We use a dataset containing various mostly cryogenic waveforms detected by an STA/LTA short-term average/long-term average algorithm on continuous waveform recordings from the geophysical observatory at Neumayer station, Antarctica. The quality of the features is evaluated on a hand-labeled dataset that includes icequakes, earthquakes, and spikes, and on a larger unlabeled dataset using a classical clustering method, k-means. Results show that the approach separates the different hand-labeled groups with an accuracy of up to 88% and separates meaningful groups within the unlabeled data. Thus, we provide an effective tool for the unsupervised exploration of large seismological datasets and the automated compilation of event catalogs.

Mathematical enhancement of unsupervised machine learning algorithms for optimal lifeline donor knowledge extraction

Lifeline Donor groups are crucial in the field of humanitarian aid because they provide lifesaving aid in times of crisis. For effective resource management and aid coordination, it is crucial to draw relevant insights from donor data in a timely manner. Scalability, precision, and flexibility in responding to changing donor dynamics are all areas where traditional techniques of knowledge extraction fall short. This study offers a fresh method for optimizing knowledge extraction from Lifeline Donor databases by utilizing improved unsupervised machine learning methods. In order to extract, categorize, and prioritize donor knowledge from disparate data sources, this research presents a multi-faceted framework that includes cutting-edge machine learning algorithms. By merging cutting-edge developments in NLP and data mining, the proposed framework improves upon classic clustering and topic modelling methods. The program is able to capture subtle semantic links and underlying patterns in donor communication data by using methods like word embedding models and graph-based clustering.

Fuzzy Geographically Weighted Clustering Analysis for Sectoral Potential Gross Regional Domestic Product in West Sumatera

Gross Regional Domestic Product is the sum of the added value of all goods and services produced or produced in an area that arises as a result of various economic activities in a certain period. Each region certainly has its own advantages and potential, such as in sectors or business fields. GRDP inequality occurs due to differences in geographical conditions and natural resources in each region. The method that can be used to overcome this inequality is cluster analysis. Fuzzy Geographically Weighted Clustering is a clustering method which is an integration of the classical fuzzy clustering method and geo-demographic elements, so that the clusters formed will be sensitive to geographic effects. The results of the research obtained 3 optimum clusters with different characteristics. Cluster 1 has high potential, cluster 2 has low potential and cluster 3 has medium potential in forming GRDP.

A clustering method based on multi-positive–negative granularity and attenuation-diffusion pattern

As an important part of machine learning, clustering methods have been continuously paid attention to. Current clustering methods divide data objects usually based on Euclidean metric, which is a basic and effective metric method. However, with the high dimensionality of data and the diversification of data representation, the complexity of the spatial structure of real-world data continues to rise. Classical clustering methods face many challenges such as insufficient clustering effectiveness, the sensitivity of clustering method parameters, and lack of stability of clustering results. Aiming at the above problems, this paper designs a non-Euclidean metric and constructs a multi-granularity staged clustering method based on the metric. First of all, this paper uses the sequential relationship of each feature of the data to construct a similarity measure between objects from the perspective of positive and negative granularity to improve the clustering algorithm’s understanding of complex spatial structure data. Secondly, this paper designs the attenuation-diffusion pattern divides and conquers according to the distribution characteristics of data objects in different patterns, and uses the heuristic idea to effectively cluster the data in stages from local to global. Again, based on the above, this paper proposes a clustering method based on multi-positive-negative granularity and attenuation-diffusion pattern, which can effectively deal with the challenges brought by complex spatial structure data to clustering methods. Finally, the effectiveness and robustness of the proposed method and advanced clustering methods are compared and analyzed on UCI real data sets. Experimental results show that the method proposed in this paper has obvious advantages in clustering results on complex spatial structure data. In addition, in the two directions of non-Euclidean metrics and multi-granularity clustering, the method proposed in this paper provides a new perspective for effectively dealing with the design of clustering methods on complex spatial structure data.

NonPC: Non-parametric clustering algorithm with adaptive noise detecting

Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.

Algebraic Multi-Layer Network: Key Concepts.

The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an NP-hard problem of calculation of close to optimal piecewise constant data approximations with the smallest possible standard deviations or total squared errors (approximation errors) is solved. The solution is achieved by revisiting, modernizing, and combining classical Ward's clustering, split/merge, and K-means methods. The concepts of objects, images, and their elements (superpixels) are formalized as structures that are distinguishable from each other. The results of structuring and ordering the image data are presented to the user in two ways, as tabulated approximations of the image showing the available object hierarchies. For not only theoretical reasoning, but also for practical implementation, reversible calculations with pixel sets are performed easily, as with individual pixels in terms of Sleator-Tarjan Dynamic trees and cyclic graphs forming an Algebraic Multi-Layer Network (AMN). The detailing of the latter significantly distinguishes this paper from our prior works. The establishment of the invariance of detected objects with respect to changing the context of the image and its transformation into grayscale is also new.

Multi-view Ensemble Clustering via Low-rank and Sparse Decomposition: From Matrix to Tensor

As a significant extension of classical clustering methods, ensemble clustering first generates multiple basic clusterings and then fuses them into one consensus partition by solving a problem concerning graph partition with respect to the co-association matrix. Although the collaborative cluster structure among basic clusterings can be well discovered by ensemble clustering, most advanced ensemble clustering utilizes the self-representation strategy with the constraint of low-rank to explore a shared consensus representation matrix in multiple views. However, they still encounter two challenges: (1) high computational cost caused by both the matrix inversion operation and singular value decomposition of large-scale square matrices; (2) less considerable attention on high-order correlation attributed to the pursue of the two-dimensional pair-wise relationship matrix. In this article, based on low-rank and sparse decomposition from both matrix and tensor perspectives, we propose two novel multi-view ensemble clustering methods, which tangibly decrease computational complexity. Specifically, our first method utilizes low-rank and sparse matrix decomposition to learn one common co-association matrix, while our last method constructs all co-association matrices into one third-order tensor to investigate the high-order correlation among multiple views by low-rank and sparse tensor decomposition. We adopt the alternating direction method of multipliers to solve two convex models by dividing them into several subproblems with closed-form solution. Experimental results on ten real-world datasets prove the effectiveness and efficiency of the proposed two multi-view ensemble clustering methods by comparing them with other advanced ensemble clustering methods.

A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. This research provides a modern, thorough review of both classic and cutting-edge clustering methods. The taxonomy of clustering is presented in this review from an applied angle and the compression of some hierarchical and partitional clustering algorithms with various parameters. We also discuss the open challenges in clustering such as computational complexity, refinement of clusters, speed of convergence, data dimensionality, effectiveness and scalability, data object representation, evaluation measures, data streams, and knowledge extraction; scientists and professionals alike will be able to use it as a benchmark as they strive to advance the state-of-the-art in clustering techniques.

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.

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.