AutoML is enabling many practitioners to use sophisticated Machine Learning pipelines even without being experienced in building application-specific solutions. Adapting AutoML to the field of unsupervised learning, particularly to the task of cluste...
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estima...
In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as...
This paper proposes a clustering method based on a randomized representation of an ensemble of possible clusters with a probability distribution. The concept of a cluster indicator is introduced as the average distance between the objects included in...
Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its n...
Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering...
In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to clust...
Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software ser...
In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evi...
Clustering validation is applied to evaluate the quality of classifications. This step is crucial for unsupervised machine learning. A plethora of methods exist for this purpose; however, a common drawback is that statistical inference is not possibl...
When an event occurs in the real world, numerous news reports describing this event start to appear on different news sites within a few minutes of the event occurrence. This may result in a huge amount of information for users, and automated process...
In this article, we evaluate the efficiency and performance of two clustering algorithms:
We introduce a new approach to clustering categorical data: Condorcet clustering with a fixed number of groups, denoted
The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for...
Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters. In this paper, advanced visualization techniques are presented that aim to facil...
A chemical reaction and its reaction environment are intrinsically linked, especially within the confines of narrow cellular spaces. Traditional models of chemical reactions often use differential equations with concentration as the primary variable,...
As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for...
The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids....
The evaluation of clustering results plays an important role in clustering analysis. Most existing indexes are designed for the evaluation of results from the most-used K-means clustering algorithm; it can identify only spherical clusters rather than...
Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data...
In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be o...
A scholarly article often discusses multiple research issues. The clustering of scholarly articles based on research issues can facilitate analyses of related articles on specific issues in scientific literature. It is a task of overlapping clusterin...
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies...
Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters....
Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well i...
Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is d...
The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms alwa...
The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk y...
This paper presents potential improvements through utilizing the cyclical nature of clusters by human capital, technology, policies, and management. A historical review of the formation and sustainable development of clusters in the US, Europe, Japan...
Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a con...
Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data. To overcome this problem,...
Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the...
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular an...
Hierarchical clustering has been extensively used in practice, where clusters can be assigned and analyzed simultaneously, especially when estimating the number of clusters is challenging. However, due to the conventional proximity measures recruited...
In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We...
Deep clustering aims to boost clustering performance by learning powerful representations via deep learning. Despite their superiority over conventional shallow algorithms, autoencoder-based methods are typically hindered by heavy dependencies on lar...
The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering algorithms are used to group samples into a number of classes or clusters based on the distances of the given sample features. Therefore, how to define th...
Block modeling is an effective way to understand Earth’s crustal deformation. However, the choice of block boundaries and the number of blocks affect the model results. Therefore, the subjectivity of this analysis should be avoided. Clustering...
Ensemble clustering has become a widely used technique for improving robustness and accuracy by combining multiple clustering results. However, traditional ensemble clustering methods often fail to provide fair treatment between groups defined by sen...
Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned...
In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand. Finding suc...
This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients...
Research on the fairness of spectral clustering has gradually increased attention. Normally, existing methods of fair spectral clustering add a fairness constraint to the original objective function so that fairness is guaranteed. However, similar to...
We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete pr...
The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall...
The performance prediction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) project represents one of the primary challenges faced by the system. To address the performance prediction issues of the FAST hydraulic actuator cluster s...
Deep clustering aims to discover meaningful data groups by jointly learning representations and cluster probability distributions. Yet existing methods rarely consider the underlying information characteristics of these distributions, causing ambigui...
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue t...
In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is present...
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...
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