Good Clustering Research Articles

Joint NOMA Clustering and Power Allocation in IoRT-Oriented Satellite Terrestrial Relay Networks

In this paper, a joint non-orthogonal multiple access (NOMA) clustering and power allocation problem is studied to maximize the uplink total sum rate in Internet of Remote Things (IoRT)-oriented satellite terrestrial relay networks (STRNs). The joint optimization problem is a mixed-integer programming (MIP) problem, which is non-convex and NP-hard. To solve this problem, we decompose it into two subproblems and propose staged algorithms to solve them. The first subproblem is an optimal NOMA clustering problem, which is still non-convex and NP-hard. To solve the subproblem efficiently, a reinforcement learning-based dynamic clustering algorithm (RL-DCA) is proposed. Using the RL-DCA, the users are able to gradually learn the optimal NOMA clustering policy in a distributed fashion. The RL-DCA has fast convergence and its computational complexity at each user remains fixed for any network size. The second subproblem is an intra-cluster optimal NOMA power allocation problem, which is still non-convex. To solve it, we firstly convert it into a convex problem by constrait approximation, then use the Karush-Kuhn-Tucker (KKT) conditions based algorithm to find the optimal power allocation policy. The second subproblem is solved in an offline fashion, which enables low computational complexity in transmission. Simulation results show that: 1) The RL-DCA has fast convergence and enables good clustering performance; 2) The joint NOMA clustering and power allocation scheme greatly outperforms the orthogonal multiple access (OMA) scheme and other NOMA schemes in terms of total sum rate.

About Some Data Precaution Techniques For K-Means Clustering Algorithm

Clustering is a technique of creating groups of objects such that each group contains similar and unique objects. One of the most popular clustering techniques is the k-means clustering algorithm. Conventional k-means techniques may not work well for high-dimensional datasets, due to the noise, discrepancies, and outliers associated with the original dataset. However, some form of transformation is required to organize the data for clustering. Four different data pre-processing methods are applied before the clustering algorithm to make the data clean, noise-free and consistent. The impact of data pre-processing on the basic k-means clustering algorithm was tested on real-life data using some normalization techniques such as z-score, mean-max, decimal scaling, and mean absolute deviation. We find that the pre-processing before clustering yields good clustering results and significantly reduces the running time compared to the traditional techniques. We can also conclude that the mean absolute deviation is the best among the four normalization methods as it captures all clustering points.

Chaotic association feature extraction of big data clustering based on Internet of Things

This article addresses the stabilization of chaotic characteristics in abnormal data by proposing the chaotic correlation feature extraction of big data clustering based on the Internet of things. The chaotic features in big data usually show complex folding and distortion without obvious rules and order and non-synchronization. In this paper, the extracted correlation dimension is used as the chaotic feature of big data clustering. The one-dimensional time series can be extended in multi-dimensional space is analyzed based on phase space reconstruction, so as to extract the chaotic correlation dimension features. After the relevant experimental analysis, this paper mainly compares the energy consumption and processing time of the two respective algorithms. In the simulation parameter design, the time interval of big data packet generation is 0.1s, and the data is generated from the simulation time of 300s. The results obtained show that when dealing with the same amount of data, the energy consumption of this algorithm is significantly lower than that of the traditional algorithm. When dealing with the same amount of data, the time required by this algorithm is significantly lower than that of the traditional algorithm. This is because this algorithm is easy to implement and has good clustering efficiency for data, so the clustering time is short. With the gradual increase of the amount of data, the correlation dimension of this algorithm tends to be stable. While the correlation dimension of the traditional algorithm fluctuates greatly, it is revealed that the proposed approach has high data clustering efficiency and verifies the effectiveness of this algorithm.

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.

NEUROPLASTYCZNOŚĆ PO WSZCZEPIENIU IMPLANTU ŚLIMAKOWEGO – POZIOM MMP-9 W PLAZMIE I JEJ POLIMORFIZM FUNKCJONALNY – WYNIKI BADANIA KOHORTOWEGO PROSPEKTYWNEGO DZIECI Z WRODZONĄ GŁUCHOTĄ

BackgroundIf it was possible to assay biomarkers of neuroplasticity it might facilitate clinical management of deaf implanted children by identifying those among them who are at risk of speech and language rehabilitation failure. <i>MMP9</i> is a proteinase involved in neuroplasticity underlying different clinical conditions in human.Material and methodsThis was a longitudinal, prospective cohort study of 61 congenitally deaf children who underwent cochlear implantation. We investigated the genetic variants of matrix metalloproteinase 9 (<i>MMP9</i>) and plasma levels of MMP-9 that have been implicated in neuroplasticity after cochlear implantation. Auditory development was assessed by using the LittlEARS Questionnaire (LEAQ) at three follow-up points and the plasma level of MMP-9 was measured at implantation.ResultsThere was a significant negative correlation between MMP-9 plasma level at implantation and LEAQ score at 18 months follow-up (<i>p</i> < 0.05). Two clusters of good and poor CI performers could be isolated based on this correlation. The prevalence of genetic variants of MMP9 – rs3918242, rs20544, and rs2234681 – in the good performers cluster was different to their prevalence in the poor performers cluster.ConclusionsThe study showed that children born deaf who have an MMP-9 plasma level of less than 150 ng/ml at cochlear implantation have a reasonable chance of attaining a high LEAQ score after 18 months of speech and language rehabilitation. It appears that MMP-9 plasma level at cochlear implantation is a promising prognostic marker for CI outcome.

Robust Identification of Subtypes in Non-Small Cell Lung Cancer Using Radiomics

Identifying the histological phenotype of non-small cell lung cancer (NSCLC) is of crucial importance to its treatment and prognosis. The radiomics-based prediction model has the potential to non-invasively extract the tumor phenotype characteristics. However, the existing research ignores the stability of extracted features, which restricts the performance and robustness of the constructed model. While most of themethods in the literature use classification accuracy to solve theproblemofradiomics featuresstability, in this paper we propose the use ofSOM (Self-organizing Mapping) and K-means to evaluate the stability of different feature subsets. The subset with good clustering performance is selected as the optimal feature subset.When the optimal feature subset is used for modeling, compared with other feature subsets, the higher AUC(Area Under Curve) and lower SD(Standard Deviation) on the three classifiers show that the feature subset had excellent classification performance and good stability, and can distinguish NSCLC subtypes more accurately and robustly.

An Automatic Identification Method of Crested Ibis (Nipponia nippon) Habitat Based on Spatiotemporal Density Detection.

Simple SummaryThe trajectory data of crested ibis (Nipponia nippon Temminck, 1835) have been obtained by HQBG3621L backpack-style tracker. By combining the spatial and temporal features of the trajectory data, an improved spatiotemporal clustering-based DBSCAN method was adopted to detect crested ibis’s stopping points and identify the crested ibis’s habitat. The clustering results are consistent with those from remote sensing images and field surveys.To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nippon Temminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.

Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique

Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination (R2) using the test set. The R2 values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.

Block preconditioners for linear systems in interior point methods for convex constrained optimization

In this paper, we address the preconditioned iterative solution of the saddle-point linear systems arising from the (regularized) Interior Point method applied to linear and quadratic convex programming problems, typically of large scale. Starting from the well-studied Constraint Preconditioner, we review a number of inexact variants with the aim to reduce the computational cost of the preconditioner application within the Krylov subspace solver of choice. In all cases we illustrate a spectral analysis showing the conditions under which a good clustering of the eigenvalues of the preconditioned matrix can be obtained, which foreshadows (at least in case PCG/MINRES Krylov solvers are used), a fast convergence of the iterative method. Results on a set of large size optimization problems confirm that the Inexact variants of the Constraint Preconditioner can yield efficient solution methods.

Block Diagonal Least Squares Regression for Subspace Clustering

Least squares regression (LSR) is an effective method that has been widely used for subspace clustering. Under the conditions of independent subspaces and noise-free data, coefficient matrices can satisfy enforced block diagonal (EBD) structures and achieve good clustering results. More importantly, LSR produces closed solutions that are easier to solve. However, solutions with block diagonal properties that have been solved using LSR are sensitive to noise or corruption as they are fragile and easily destroyed. Moreover, when using actual datasets, these structures cannot always guarantee satisfactory clustering results. Considering that block diagonal representation has excellent clustering performance, the idea of block diagonal constraints has been introduced into LSR and a new subspace clustering method, which is named block diagonal least squares regression (BDLSR), has been proposed. By using a block diagonal regularizer, BDLSR can effectively reinforce the fragile block diagonal structures of the obtained matrices and improve the clustering performance. Our experiments using several real datasets illustrated that BDLSR produced a higher clustering performance compared to other algorithms.

Ensemble learning using three-way density-sensitive spectral clustering

As one popular clustering algorithm in the last few years, spectral clustering is advantageous over most existing clustering algorithms. Although spectral clustering can perform well in many instances, the algorithm still has some problems. The clusters obtained by spectral clustering have crisp boundaries, which cannot reflect the fact that one cluster may not have a well-defined boundary in the real situations. Furthermore, the frequently-used distance measures in spectral clustering cannot satisfy both global and local consistency, especially for the data with multi-scale. In order to address the above limitations, we firstly present a three-way density-sensitive spectral clustering algorithm, which uses the core region and the fringe region to represent a cluster. In the proposed algorithm, we use density-sensitive distance to produce a similarity matrix, which can well capture the real data structures. An overlap clustering is introduced to obtain the upper bound (unions of the core regions and the fringe regions) of each cluster and perturbation analysis is applied to separate the core regions from the upper bounds. The fringe region of the specific cluster is the differences between the upper bound and the core region. Because a single clustering algorithm cannot always achieve a good clustering result, we develop an improved ensemble three-way spectral clustering algorithm based on ensemble strategy. The proposed ensemble algorithm randomly extracts feature subset of sample and uses the three-way density-sensitive clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, voting method is used to generate a three-way clustering result. The experimental results show that the three-way density-sensitive clustering algorithm can well explain the data structure and maintain a good clustering performance at the same time, and the ensemble three-way density-sensitive spectral clustering can improve the robustness and stability of clustering results.

Local Motif Clustering via (Hyper)Graph Partitioning

Local clustering consists of finding a good cluster around a seed node in a graph. Recently local motif clustering has been proposed: it is a local clustering approach based on motifs rather than edges. Since this approach is recent, most algorithms to solve it are extensions of statistical and numerical methods previously used for local clustering, while combinatorial approaches are still few and simple. In this work, we build a (hyper)graph to represent the motif-distribution around the seed node. We solve this model using sophisticated (hyper)graph partitioners. On average, our algorithm computes clusters six times faster and three times better than the state-of-the-art for the triangle motif.

Wayside Acoustic Fault Diagnosis of Train Bearing Based on Improved Linear Discriminant Analysis

Train bearing is one of the key parts of a train wheel, and its fault diagnosis is crucial for train safety. This paper proposes an Improved Linear Discriminant Analysis (ILDA) algorithm to diagnose train bearing faults. A new distance function is employed to adjust the between-class divergence matrix of LDA. The adjusted between-class divergence matrix is adopted for selecting the projection direction with the best clustering performance and reducing the ambiguity or overlap among different classes. The sample clustering evaluation index( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SI</i> ) is adopted to evaluate the clustering performance of fusion features obtained by ILDA. First, twenty-four original features are extracted from wayside acoustic signals after reducing the Doppler effect. Then, ILDA is adopted to extract fusion features. Finally, the train bearing faults are identified by extreme learning machine(ELM). The experimental results show that the fusion features extracted by the proposed method can be effectively identified, which have good clustering performance and high identification rate. Furthermore, the identification results of two datasets provided by different universities show that the fusion features obtained by ILDA also have good clustering performance and high identification rate, which further verify the validity of the proposed method.

Financial Security Risk Detection in Colleges and Universities Relying on Big Data Clustering Center Scheduling Algorithm

In order to improve the monitoring effect of financial security risk in colleges and universities, this paper studies the financial security risk control of colleges and universities combined with the big data clustering center scheduling algorithm and inverts the multilevel sampling algorithm of quantum potential support. Moreover, this paper considers that the multilevel sampling algorithm is applied to the potential backscattering problem of the stationary Schrödinger equation to invert the support of the potential in the equation. In addition, this paper uses far-field data to invert the generalized linear sampling method of potential support and builds a college financial security risk monitoring model that relies on the big data clustering center scheduling algorithm. The experimental study shows that the financial security risk monitoring system for colleges and universities based on the big data clustering center scheduling algorithm proposed in this paper has good risk clustering and risk identification effects.

Effective indexing for dynamic structural graph clustering

Graph clustering is a fundamental data mining task that clusters vertices into different groups. The structural graph clustering algorithm ( SCAN ) is a widely used graph clustering algorithm that derives not only clustering results, but also special roles of vertices like hubs and outliers. In this paper, we consider structural graph clustering on dynamic graphs under Jaccard similarity. The state-of-the-art index-based solution focuses on static graphs and incurs prohibitive update costs to maintain indices. Lately, an efficient approximate dynamic structural graph clustering algorithm Dyn-StrClu under Jaccard similarity is proposed. However, their solution needs to fix input parameters while parameter settings of SCAN usually need to be fine-tuned to achieve good clustering results. Motivated by these limitations, we present a study on devising effective index structures for SCAN algorithm on dynamic graphs. Similar to the state-of-the-art dynamic scheme, our main idea to reduce the time complexity is still by bringing approximation to clustering results. However, our solution does not need to fix the input parameters. To achieve this, our solution includes two key components. The first is to maintain a bottom- k sketch for each vertex so that the similarities of affected vertices can be easily updated. The second key is a bucketing strategy that allows us to update clustering results and roles of vertices efficiently. Our theoretical analysis shows that our proposed algorithm achieves O (log n · log M + m / pf ) expected update cost and guarantees to return approximate clustering results with probability 1 - pf after up to M updates. Extensive experiments show that our solution is up to two orders of magnitude faster than the state-of-the-art index-based solution while still achieving high-quality clustering results.

A Cluster Analysis of Population Based Cancer Registry in Brunei Darussalam: An Exploratory

Machine learning techniques have been mostly applied in gene expression cancer data. Socio-demographic data available in cancer registries could be explored, to get further insight into relationships between cancer types and their contributing factors. Moreover, less attention has been paid to analyse the mixed demographic data (numeric and categorical) from cancer registries and its association to the cancer types. The aim of this study is to identify subgroups of patients, having similar demographics characteristics, from the population based cancer registry in Brunei Darussalam and examine the prevalent cancer types in these subgroups. Four clustering algorithms are explored in the cluster analysis of Brunei Darussalam Cancer Registry; Two-step, Partitional Around Medoid, Agglomerative Hierarchical and Model-based. Gower distance was used for measuring similarity for mixed data types. To evaluate the clusters found; cluster distribution and Silhouette index were used for cluster quality, Cohen's Kappa Index for cluster stability and Cramer's V Coefficient for clinical relevance of clusters. Six distinct demographic subgroups were consistently found by three algorithms while model-based clustering solution were not considered for deeper analysis as highly imbalanced clusters were produced. The subgroups found have good quality clusters, moderate association with cancer types and high stability. The top three prevalent cancers associated with these subgroups were consistently identified using the three algorithms. Upon comparing the subgroups’ ages during diagnosis, we identify possible screening behaviours of specific subgroups, suggesting for early screening awareness programmes. This study demonstrates the use of cluster analysis in a cancer registry to identify demographic subgroups that could suggest potential areas to develop targeted and improved healthcare management strategies.

Differential Evolution Model for Identification of Most Influenced Gene in Brest Cancer Data

Microarray technology generates a large amount of data. Clustering is a popular technique for locating genes that are expressed in close proximity. It entails examining a fresh dataset to determine whether similar traits can be used to identify any hidden groupings. There are large-dimensional datasets accessible, such as those produced from gene expression investigations, RNA microarray studies, or RNA sequencing studies. As a consequence, cluster analysis and producing well-separated clusters become more challenging. Good cluster separation is desirable since it suggests that items are not being placed in the erroneous clusters. In this study, it was recommended that a Differential Evolution-based (DE) Model be used to interpret the analysis of Brest cancer gene expression. To begin, cluster the gene expression data to find the genes most likely to be impacted by the illness. The appropriate number of clusters must be found in order to locate the gene with the highest effect in the gene collection. We used a DE model on the Brest cancer datasets in this work. We identified the best number of clusters by using the most impacted gene in this dataset as a benchmark. We then experimented with different cluster sizes. We used the DE method to three distinct breast cancer datasets and compared it to the current K-Means and K-medoids models. The results of the experiments show that the proposed DE model outperforms existing models significantly.

Linearity-Aware Subspace Clustering

Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the similarity matrix through self-expressive strategy. However, these methods directly adopt original samples as a set of basis to represent itself linearly. It is difficult to accurately describe the linear relation between samples in the real-world applications, and thus is hard to find an ideal similarity matrix. To better represent the linear relation of samples, we present a subspace clustering model, Linearity-Aware Subspace Clustering (LASC), which can consciously learn the similarity matrix by employing a linearity-aware metric. This is a new subspace clustering method that combines metric learning and subspace clustering into a joint learning framework. In our model, we first utilize the self-expressive strategy to obtain an initial subspace structure and discover a low-dimensional representation of the original data. Subsequently, we use the proposed metric to learn an intrinsic similarity matrix with linearity-aware on the obtained subspace. Based on such a learned similarity matrix, the inter-cluster distance becomes larger than the intra-cluster distances, and thus successfully obtaining a good subspace cluster result. In addition, to enrich the similarity matrix with more consistent knowledge, we adopt a collaborative learning strategy for self-expressive subspace learning and linearity-aware subspace learning. Moreover, we provide detailed mathematical analysis to show that the metric can properly characterize the linear correlation between samples.

Effect of No Tillage System on Soil Fungal Community Structure of Cropland in Mollisol: A Case Study.

Conservation tillage is generally regarded as a sustainable farming system for the future. The fungal community structure has a strong response to conservation tillage. However, how the conservation tillage system affects the soil fungal community structure is little known. Using the high-throughput sequencing technology, the soil fungal community was explored under no tillage (NT) and conventional tillage (CT) in Northeast China Mollisol. The copy number of fungal genes in NT20 was significantly lower than that in other treatments. NT changed the composition of soil fungal communities at the taxonomic level of phylum and genus. The diversity indices of the soil fungal community in no tillage at soil depths of 0–5 cm (NT5) were significantly higher than those in soil depths of 5–20 cm (NT20). The fungal community under NT and CT could form a good cluster distribution and NT5, conventional tillage at soil depths of 0–5 cm (CT5) and 5–20 cm (CT20) had specific indicator species. Most of the potential pathogens were significantly higher in NT5 than in NT20. Tillage and soil depth could explain 64% of the diversity and 95% of the composition of the fungal community, which indirectly changed the diversity and composition of fungi by using soil organic carbon, pH value, and soil bulk density. Furthermore, soil organic carbon (SOC) best explained the soil fungal community, followed by soil pH. The study indicated that the NT system had a comprehensive effect on the soil fungal community and SOC is the most crucial factor in determining this community.

AAE-Dpeak-SC: A novel unsupervised clustering method for space target ISAR images based on adversarial autoencoder and density peak-spectral clustering

Aiming at the problem of unlabeled ISAR image clustering of space targets, the paper proposes a new unsupervised clustering method based on adversarial autoencoder (AAE) and density peak-spectral clustering (Dpeak-SC). This method introduces the AAE into the unsupervised clustering problem of radar images for the first time and realizes the automatic extraction of the features of unlabeled space target ISAR image data. And on this basis, by combining the density peak algorithm with the spectral clustering algorithm, the ISAR image data is classified into multiple clusters with good clustering accuracy, clustering coincidence, and clustering similarity without providing any prior information. This method first implements the self-supervised training of the AAE model through the adversarial and reconstruction process. Then, it uses the trained encoder to extract the latent features of the ISAR image of the space target in the low-dimensional space. Next, a decision graph is constructed by calculating the local density and relative distance of the proposed features to determine the number of clusters in the hidden feature vector set. Finally, the spectral clustering algorithm is used to transform the problem into the graph's optimal partition problem. The clustering is realized by constructing and segmenting the undirected weight graph of the hidden feature vector set. The simulation test on the unlabeled space target ISAR image data verifies the method's effectiveness in this paper.