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...
The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clus...
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...
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...
The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a...
Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical...
Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and ca...
The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algori...
Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters...
Multiple cluster validity indices (CVIs) have been introduced for diverse applications. In practice, clusters exhibit varying shapes, sizes, densities, and closely spaced centers, which are typically unknown beforehand. It is desirable to develop a v...
The number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis. The number of classes is usually determined by cluster validity indices (CVIs)....
Cluster Validity Indices (CVIs) act as a pivotal tool in machine learning for assisting in the determination of the optimal number of clusters. Nevertheless, traditional CVIs often exhibit subpar performance when confronted with the complex character...
Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have...
Cluster validation for categorical data remains a critical challenge in unsupervised learning, where traditional distance-based indices often fail to capture meaningful structures. This paper introduces SLEDgeHammer (SLEDgeH), an enhanced internal va...
Clustered cross-national panels pose two problems: observation-level validation can be optimistic when the same economy appears in training and evaluation, while standard split conformal coverage requires exchangeability that pooled cells from hetero...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number...
Europe measures the innovative performance of its regions largely by counting patents, and allocates public resources accordingly. This paper asks what regional patenting reflects, using Regional Innovation Scoreboard data for 245 European regions ob...
The co-occurrence of sports and artificial intelligence in online search results does not necessarily indicate meaningful relevance of AI applications in sports. This study examined source-specific lexical salience in Korean online information and as...
The Central Sensitization Inventory (CSI) measurement properties in patients having nonspecific, noncancer pain are well-established. However, studies examining the reliability and validity of either the CSI or the Central Sensitization Inventory sho...
Background: The neonatal intensive care unit (NICU) is one of medicine’s most data-rich Internet of Things (IoT) environments, and monitoring digital twins (DTs) that predict clinical events from continuously acquired physiologic streams are a...
Unhealthy eating is associated with various diseases, such as cardiovascular, neurodegenerative, or oncological. There are neither economical nor behavior-related questionnaires available in the German language to assess general dietary behavior. The...
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. Howe...
The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions...
1 March 2022
In medical science and practice, dietary behavior is mostly assessed by item-extensive questionnaires (e.g., food-frequency-questionnaires) or by questionnaires focusing on psychological aspects of dietary behavior neglecting dietary quality or quant...
Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, simi...
Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of genes at single-cell resolution in complex disease or tissue. While many methods have been developed to detect cell clusters from the scRNA-seq data, this...
Unsupervised clustering of deep image features is often proposed for unlabeled image archives, yet it is usually judged by internal criteria rather than by agreement with expert annotations. In a single domain of texture images, namely full-size rock...
Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown scheme...
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...
Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system,...
Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spa...
The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters...
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, a...
In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in...
Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular...
Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characterist...
Regional tuberculosis (TB) surveillance requires analytical tools that distinguish outcome definitions, preserve temporal ordering, and characterize geographic heterogeneity. This study developed an information system integrating regression modeling...
(1) Dietary behavior is highly relevant for patients after bariatric surgery. No instrument exists assessing adherence to medical guidelines concerning the dietary behavior of patients after bariatric surgery. The aim of this study was to develop and...
Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clu...
(1) Background: Reference-interval flags read a lipid–glucose panel one analyte at a time. We asked whether a routine five-analyte panel supports reproducible, transportable, auditable descriptive grouping of laboratory profiles. (2) Methods: R...
Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant econo...
Although topic models have been used to build clusters of documents for more than ten years, there is still a problem of choosing the optimal number of topics. The authors analyzed many fundamental studies undertaken on the subject in recent years. T...
CO2 adsorption on subnanometric metal clusters is highly sensitive to the computational protocol used to describe the potential energy surface, particularly when several low-lying geometries and spin states are accessible. In this work, CO2 adsorptio...
Increasing turbulence in contemporary business environments has made the quantitative analysis of unstructured textual data a central methodological challenge for researchers and decision-makers. The increasing availability of large-scale textual dat...
Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a n...
Electrodialysis (ED) may be designed to enhance wastewater recycling efficiency for crop irrigation in areas where water distribution is otherwise inaccessible. ED process controls are difficult to manage because the ED cells need to be custom-built...
Background/Objectives: Detecting Alzheimer’s disease (AD) from normal aging using eyes-open (EO) EEG is challenging due to stimulus-driven nonstationarity and fragmented oscillatory responses. This study aims to determine whether prototype-base...
Architectural colour in public libraries significantly influences indoor atmospheric quality and occupant emotional well-being, yet palette selection in design practice remains predominantly reliant on subjective intuition, precedents, and vendor cat...
Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realis...
19 January 2021
In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples...
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