Clustering Criterion Research Articles

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases.

Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses

BackgroundA key step for comparative genomics is to group open reading frames into functionally and evolutionarily meaningful gene clusters. Gene clustering is complicated by intraspecific duplications and horizontal gene transfers that are frequent in prokaryotes. In consequence, gene clustering methods must deal with a trade-off between identifying vertically transmitted representatives of multicopy gene families, which are recognizable by synteny conservation, and retrieving complete sets of species-level orthologs. We studied the implications of adopting homology, orthology, or synteny conservation as formal criteria for gene clustering by performing comparative analyses of 125 prokaryotic pangenomes.ResultsClustering criteria affect pangenome functional characterization, core genome inference, and reconstruction of ancestral gene content to different extents. Species-wise estimates of pangenome and core genome sizes change by the same factor when using different clustering criteria, allowing robust cross-species comparisons regardless of the clustering criterion. However, cross-species comparisons of genome plasticity and functional profiles are substantially affected by inconsistencies among clustering criteria. Such inconsistencies are driven not only by mobile genetic elements, but also by genes involved in defense, secondary metabolism, and other accessory functions. In some pangenome features, the variability attributed to methodological inconsistencies can even exceed the effect sizes of ecological and phylogenetic variables.ConclusionsChoosing an appropriate criterion for gene clustering is critical to conduct unbiased pangenome analyses. We provide practical guidelines to choose the right method depending on the research goals and the quality of genome assemblies, and a benchmarking dataset to assess the robustness and reproducibility of future comparative studies.

The use of restricted Boltzmann machines for clustering collaborative filtering

<div><p class="Abstract">Collaborative filtering-based recommender systems have been successfully serviced through commercial online systems to assist people with searching the information useful to them. However, several problems inherent in such systems still exist, although a lot of research work has been devoted to finding solutions. This work focuses on clustering collaborative filtering to address the scalability problem. It proposes a novel method to determine the clustering criteria for enhancing the prediction and recommendation accuracy of the systems, which is typically degraded when the clustering algorithm is integrated into collaborative filtering. We use a restricted Boltzmann machine to find the genre preference of users, which is then inputted into the clustering algorithm to cluster users. Various experiments are conducted to evaluate the performance of the proposed method. As a result, our method showed superior performance in terms of various performance criteria compared to previous clustering collaborative filtering methods and some of the major traditional systems.</p></div>

Local neighbor propagation on graphs for mismatch removal

Recently, despite the promising progress having been achieved, most mismatch removal methods only consider the connection relationships of feature matches in a single neighborhood, and ignore their relationships between different neighborhoods, which will lead to the unavailability of local topological structure for feature matching. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based mismatch removal (LNPG) method for robust feature matching. LNPG starts from a novel neighborhood graph construction strategy, which leverages both the spatial and the residual information to preserve the local neighborhood structures of potential inliers. Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connection relationships of the data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm, which introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for robust clustering. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Extensive experiments on publicly available datasets show the superiority of our LNPG in comparison with other state-of-the-art methods.

Identifying and Protecting Cyber-Physical Systems’ Influential Devices for Sustainable Cybersecurity

For sustainable cyber-physical systems (CPS) security, proactive measures to cybersecurity need to be implemented instead of reactive measures. Towards this, we introduce in this paper a proactive methodology implemented in a system called IDI_CPS. It is based on the observation that CPS devices that have LAN-based network sharing (e.g., via Wi-Fi connections) need first to be clustered using some clustering criterion. Then, the influential and central devices in these clusters need to be identified to pay more attention to their file sharing. These influential devices may have network sharing with devices at the WAN level. Therefore, the influential devices at the WAN level that have network sharing with the influential devices in the clusters need also to be identified to pay more attention to their file sharing. We propose novel techniques for: (1) clustering the devices that have LAN-based network sharing using k-clique modeling, (2) employing clustering coefficient-based techniques for identifying the most influential device in each cluster, and (3) employing Independent Cascades model-based techniques for identifying the influential devices at the WAN level that have network sharing with the influential devices in the clusters. We experimentally evaluated our proposed system IDI_CPS and compared it with four comparable methods. Results showed marked improvement.

Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): 1) Information credibility directly determines the reliability of clustering results and final decision output; 2) Consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this paper proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. Firstly, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of “916” Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.

Deep sample clustering domain adaptation for breast histopathology image classification

Deep learning has been widely applied to the diagnosis of breast cancer. Most deep learning models require a large number of labeled samples for training, but a large number of datasets with detailed labels is impractical. Therefore, exploring the methods to accurately classify unlabeled data remains a crucial area of research. In this paper, a deep sample clustering unsupervised domain adaptation method based on deep fusion feature is proposed for breast histopathology image classification. Deep multi-level fusion feature has a strong ability to capture image information, allowing for accurate characterization of the structural features of cells, thus improving the clustering performance. Next, based on the criterion of adaptive clustering of samples, the similarity between different clustering centers in the source and target domains is adjusted jointly. Finally, the classification results were achieved by the nearest neighbor relationship between the clustering centers and samples in the target domain. The proposed method was tested on three different breast cancer datasets. The prediction results of all patches were aggregated to obtain the classification accuracy of the histopathological image. The experimental results demonstrate that the proposed method achieves high classification accuracy and has the potential for application in clinical settings.

The use of restricted Boltzmann machines for clustering collaborative filtering

<div><p class="Abstract">Collaborative filtering-based recommender systems have been successfully serviced through commercial online systems to assist people with searching the information useful to them. However, several problems inherent in such systems still exist, although a lot of research work has been devoted to finding solutions. This work focuses on clustering collaborative filtering to address the scalability problem. It proposes a novel method to determine the clustering criteria for enhancing the prediction and recommendation accuracy of the systems, which is typically degraded when the clustering algorithm is integrated into collaborative filtering. We use a restricted Boltzmann machine to find the genre preference of users, which is then inputted into the clustering algorithm to cluster users. Various experiments are conducted to evaluate the performance of the proposed method. As a result, our method showed superior performance in terms of various performance criteria compared to previous clustering collaborative filtering methods and some of the major traditional systems.</p></div>

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.

Extinction and Independent Scattering Criterion for Clusters of Spherical Particles Embedded in Absorbing Host Media

In practical applications, the independent scattering approximation (ISA) is widely used to analyze light transfer in nanoparticle systems. However, the traditional independent scattering criterion is obtained under the assumption that the host medium surrounding particles is nonabsorbing, and thus may be invalid in certain circumstances. In this work, to explore the applicability of the ISA for small particles in absorbing host media, we calculate the extinction efficiency of particle clusters by direct solutions of macroscopic Maxwell equations. Using the far-field and distance-independent definitions of extinction, the computational efficiency multi-sphere method is applied for particle clusters in absorbing host, and its accuracy is verified with the discrete dipole approximation method. It is well known that for small particles, the dependent scattering in transparent host always enhances the extinction of the cluster and the criterion for the ISA is nearly independent of the particle refractive index and particle size. We show, however, that when the host medium is absorbing, the dependent scattering between particles can lead to a decreased or even negative extinction, and thus the ISA criterion depends on the particle refractive index, size, and host medium absorption index. In this result, the generalized criteria for absorbing host media may differ significantly from the conventional ones for transparent host media. The results can provide guidance in solving problems related to light transfer in nanoparticle systems, particularly in the presence of absorption in the host medium.

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers.

Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct. The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group. The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect. This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions.

Recurrent general type-2 fuzzy neural networks for nonlinear dynamic systems identification

This paper introduces a recurrent general type-2 Takagi–Sugeno–Kang fuzzy neural network (RGT2-TSKFNN) for the identification of nonlinear systems. In the proposed structure, the general type-2 fuzzy set (GT2FS) and a recurrent fuzzy neural network (RFNN) are combined to obviate the data uncertainties. The fuzzy firing strengths in the developed structure are returned to the network input as internal variables. In the proposed structure, GT2FS is utilized to characterize the antecedent parts while the consequent parts are performed using TSK type. The issues of constructing a RGT2-TSKFNN involve type reduction, structure learning as well as parameter learning. An efficient strategy is developed by utilizing alpha-cuts to decompose a GT2FS into several interval type-2 fuzzy sets (IT2FSs). In order to solve the computation time of the type-reduction issue, a direct defuzzification method is used instead of iterative nature of Karnik–Mendel​ (KM) algorithm. Type-2 fuzzy clustering and Lyapunov criteria are utilized for online structure learning as well as the antecedent and consequent parameters, respectively for reducing the number of rules and guaranteeing the stability of the proposed RGT2-TSKFNN. The reported comparative analysis of the simulation results is utilized to estimate the performance of the proposed RGT2-TSKFNN with respect to other popular type-2 FNNs (T2FNNs) methodologies.

An MDS-based unifying approach to time series K-means clustering: application in the dynamic time warping framework

Partitioning algorithms, and in particular K-means clustering, are widely used in time series analysis. K-means clustering is intrinsically related to the use of the Euclidean distance as a measure of dissimilarity. When other dissimilarity measures, such as dynamic time warping, are involved, K-means clustering is usually replaced by the optimisation of a sums-of-the-stars clustering criterion, giving rise to an algorithm other than that of K-means, such as K-medoids. Another common restriction in the implementation of K-means concerns the need to estimate the average as the cluster prototype, which may represent a drawback for this method in time series when elastic measures such as dynamic time warping are used. In this paper, we propose a multidimensional scaling based K-means clustering algorithm that enables the use of K-means clustering together with any dissimilarity measure, and in particular with dynamic time warping, without requiring us to estimate cluster prototypes for the time series. This procedure is a true K-means clustering algorithm that searches for the partition in an equivalent auxiliary configuration, usually in a dimension lower than the time series length. The approach proposed is of particular interest when dynamic time warping is used in the analysis of series of unequal length and/or when some data are missing, and hence Euclidean distances cannot be used. The performance of our procedure is tested by conducting an extensive Monte Carlo experiment, comparing the results with those obtained by K-medoids. The procedure is also illustrated with the analysis of carbon dioxide emissions from 133 countries.

Clustering of football players based on performance data and aggregated clustering validity indexes

Abstract We analyse football (soccer) player performance data with mixed type variables from the 2014-15 season of eight European major leagues. We cluster these data based on a tailor-made dissimilarity measure. In order to decide between the many available clustering methods and to choose an appropriate number of clusters, we use the approach by Akhanli and Hennig (2020. “Comparing Clusterings and Numbers of Clusters by Aggregation of Calibrated Clustering Validity Indexes.” Statistics and Computing 30 (5): 1523–44). This is based on several validation criteria that refer to different desirable characteristics of a clustering. These characteristics are chosen based on the aim of clustering, and this allows to define a suitable validation index as weighted average of calibrated individual indexes measuring the desirable features. We derive two different clusterings. The first one is a partition of the data set into major groups of essentially different players, which can be used for the analysis of a team’s composition. The second one divides the data set into many small clusters (with 10 players on average), which can be used for finding players with a very similar profile to a given player. It is discussed in depth what characteristics are desirable for these clusterings. Weighting the criteria for the second clustering is informed by a survey of football experts.

Adaptive multi-granularity sparse subspace clustering

Sparse subspace clustering (SSC) focuses on revealing data distribution from algebraic perspectives and has been widely applied to high-dimensional data. The key to SSC is to learn the sparsest representation and derive an adjacency graph. Theoretically, the adjacency matrix with proper block diagonal structure leads to a desired clustering result. Various generalizations have been made through imposing Laplacian regularization or locally linear embedding to describe the manifold structure based on the nearest neighborhoods of samples. However, a single set of nearest neighborhoods cannot effectively characterize local information. From the perspective of granular computing, the notion of scored nearest neighborhoods is introduced to develop multi-granularity neighborhoods of samples. The multi-granularity representation of samples is integrated with SSC to collaboratively learn the sparse representation, and an adaptive multi-granularity sparse subspace clustering model (AMGSSC) is proposed. The learned adjacency matrix has a consistent block diagonal structure at all granularity levels. Furthermore, the locally linear relationship between samples is embedded in AMGSSC, and an enhanced AMGLSSC is developed to eliminate the over-sparsity of the learned adjacency graph. Experimental results show the superior performance of both models on several clustering criteria compared with state-of-the-art subspace clustering methods.

Speaker Counting Based on a Novel Hive Shaped Nested Microphone Array by WPT and 2D Adaptive SRP Algorithms in Near-Field Scenarios.

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman-Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation.

IMPROVING ROBUSTNESS OF DEEP NETWORKS USING CLUSTER-BASED ADVERSARIAL TRAINING

Deep learning models have been found to be susceptible to adversarial attacks, which limits their use in security-sensitive applications. One way to enhance the resilience of these models is through adversarial training, which involves training them with intentionally crafted adversarial examples. This study introduces the idea of clustering-based adversarial training technique, with preliminary results and motivations. In this approach, rather than using adversarial instances directly, they are first grouped using various clustering algorithms and criteria, creating a new structured space for model training. The method's performance is evaluated on the MNIST dataset against different adversarial attacks, such as FGSM and PGD, with an examination of the accuracy-robustness trade-off. The results show that cluster-based adversarial training could be used as a data augmentation method to enhance the generalization in both clean and adversarial domains.

A new method based on ensemble time series for fast and accurate clustering

PurposeThe common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.Design/methodology/approachFirst, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.FindingAccording to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.Originality/valueThis paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

Validation of the Danish PTSD Checklist for DSM-5 in trauma-exposed chronic pain patients using the Clinician-Administered PTSD Scale for DSM-5

ABSTRACT Background: Validation of post-traumatic stress disorder (PTSD) screening tools across various populations to ensure accurate PTSD estimates is important. Because of the high symptom overlap between PTSD and pain, it is particularly important to validate PTSD screening tools in trauma-exposed chronic pain patients. Objective: The present study is the first seeking to validate the PTSD Checklist for DSM-5 (PCL-5) in a sample of trauma-exposed, treatment-seeking chronic pain patients. Method: The validation and optimal scoring of the PCL-5 were investigated using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) in chronic pain patients exposed to traffic or work-related traumas (n = 84). Construct validity was investigated using confirmatory factor analyses testing six competing DSM-5 models in a sample of mixed trauma-exposed chronic pain patients (n = 566), and a subsample of chronic pain patients exposed to traffic or work-related trauma only (n = 202). Furthermore, concurrent validity and discriminant validity were investigated using correlation analysis. Results: The results showed moderate (κ = .46) diagnostic consistency between the PCL-5 and the CAPS-5 using the DSM-5 symptom cluster criteria, and the overall accuracy of the scale (area under the curve = .79) was highly acceptable. Furthermore, the Danish PCL-5 showed excellent construct validity both in the full sample and in the subsample of traffic and work-related accidents, with superior fit of the seven-factor hybrid model. Excellent concurrent validity and discriminant validity were also established in the full sample. Conclusion: The PCL-5 appears to have satisfactory psychometric properties in trauma-exposed, treatment-seeking chronic pain patients.

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation.