Clustering Index Research Articles

Vibrating behavior of submerged floating tunnel in current field investigated with hybrid vector-autoregressive model

This paper presents a study for identifying damage severity and location in the supporting system of tether-type submerged floating tunnel (SFT) as an innovative manner for crossing a variety of water area. A novel sensitive damage detection approach is proposed in the context of statistical pattern recognition to analyze the vibrating behaviors of the main tube and the tether system of SFT induced by currents acting upon both the tube and the tethers by making use of the time-domain displacement and force series obtained from numerical simulations based on smoothed particle method (SPH). From clustering of the constructed potential damage indicators fitted to SFT responses in currents, different damage conditions can be recognized qualitatively with the severity quantified by two clustering indices. Clustering patterns of the damage indicators provides the possibility of identifying damages in a certain tether when missing the direct force records of the tether in question. The damage information can be detected by the locations and separations of the probability density distribution curves based on the Mahalanobis distances generated from the damage indicators. An insight into the antagonistic and balancing effects between the tethers in the supporting system of SFT can also be taken with the Mahalanobis distance distributions.

Comparing EQ-5D-3L and EQ-5D-5L in measuring the HRQoL burden of 4 health conditions in China.

EQ-5D-3L has been used in the National Health Services Survey of China since 2008 to monitor population health. The five-level version of EQ-5D was developed, but there lacks evidence to support the use of five-level version of EQ-5D in China. This study was conducted to compare the measurement properties of both the EQ-5D-3L and EQ-5D-5L in quantifying health-related quality of life (HRQoL) burden for 4 different health conditions in China. Participants from China were recruited to complete the 3L and 5L questionnaire via Internet. Quota was set to recruit five groups of individuals, with one group of individuals without any health condition and one group of generalized anxiety disorder (GAD), HIV/AIDS, chronic Hepatitis B (CHB), or depression, respectively. The 3L and 5L were compared in terms of response distributions, percentages of reporting 'no problems', index value distributions, known-group validity and their relative efficiency. In total, 500 individuals completed the online survey, including 140 healthy individuals, 122 individuals with hepatitis B, 107 with depression, 90 individuals with GAD and 101 with HIV/AIDS. 5L also had smoother and less clustered index value distributions. Healthy group showed different response distributions to the four condition groups. The percentage of reporting 'no problems' decreased significantly in the 5L in all domains (P < 0.01), especially in the pain/discomfort dimension (relative difference: 43.10%). Relative efficiency suggested that 5L had a higher absolute discriminatory power than the 3L version between healthy participant and the other 4 condition groups, especially for the HIV/AIDS group when the 3L results was not significant. The 5L version may be preferable to the 3L, as it demonstrated superior performance with respect to higher sensitivity to mild health problems, better relative efficiency and responses and index value distributions.

Performance evaluation of the particle swarm optimization for clustering based on different parameter selection

Particle Swamp Optimization (PSO) is an effective method for solving a wide range of problems. However, the most existing PSO algorithms easily trap into local optima when solving complex multimodal function optimization problems. In this paper, we explain the importance of PSO algorithm’s general purposes to optimize strategy which has various parameters that decide its conduct and viability in advancing a given issue. This study gives a rundown of the best selections of parameters for different advancement situations which should enable the specialist to accomplish better outcomes with less exertion. In this paper, we define an important in the PSO algorithm, the parameters and how to apply this algorithm to different type of datasets online and offline. Repeat Consumption Matrices Dataset has been tested with PSO algorithm to define the number of clusters using selection of parameters. The results are then having been compared with the Genetic Algorithm (GA) where the algorithms have been tested based on the number of cluster and DB index for each of the data. Based on the results, it is shown that the PSO algorithm gives good and efficient results compared to GA algorithm. The output of the research is a PSO-based clustering algorithm that can be used in data mining by providing accurate and robust results in clustering, which can be realized in web search engines and automatic doc-ument organization.

CONFIGURATION OF MORPHOLOGICAL DISSIMILARITIES AND ASSEMBLY ORDINATION IN CORIANDER (Coriandrum sativum L.)

The genotypes investigated had a lot of variance in all of the morphological features, according to the analysis of genotype variability. The most significant variance was found in biomass, followed by the number of primary branches and umbels per plant. The number of days to maturity and the number of days to 50% flowering showed the least variation. The coefficients of variation, as well as the signs used for metroglyph, are included in the range of variability. The results of the investigation revealed that the index scores ranged from 12 to 27. The genotype clusters of similarity were identified by plotting the distribution of index scores. A total of 128 genotypes were grouped into four primary clusters. The cluster identity was also discovered by analysing the clusters for contributory qualities for the index scores. Major contributory features for each cluster's index scores. Anderson's metroglyph is a technique for grouping genotypes that is reasonably straightforward.

Construction Method of the Distribution Transform Load Feature Database Based on Deep Convolutional Autoencoder

With the large-scale access of distributed resources to distribution network operation, there are more and more prosumers on the user side. It forms the basis of load prediction and demand-side management to identify different power consumption patterns and establish a typical load characteristic database according to the load data of prosumers. Therefore, a method to build a prosumer load characteristic database based on a deep convolutional autoencoder is proposed. First, the autoencoder network was used to extract the features of the load data collected to reduce the data dimension. Then, the density weight canopy algorithm was used to precluster the data after dimensionality reduction to obtain the initial clustering center and the optimal clustering number K value. The pre-clustering results were combined with the k-means algorithm for clustering, and the typical load characteristic database of prosumers was obtained. Finally, the comparison between the clustering index and the traditional k-means clustering algorithm and the improved k-means direct clustering algorithm proves that the method can effectively improve the accuracy of clustering results.

Parallel complete gradient clustering algorithm and its properties

Clustering is one of the most important tasks in the field known as ‘Exploratory Data Analysis’ (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection – which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering.

Quantization of Product using Collaborative Filtering Based on Cluster

Abstract: Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user’s preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. In this paper we also propose similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties. For this we use a clustering method to find groups of similar objects. Index Terms: Product Quantization, Clustering, Product Search, Collaborative Filtering

Computer-Vision-Based Indexes for Analyzing Broiler Response to Rearing Environment: A Proof of Concept.

Simple SummaryWe tested two computer-vision-based indexes to analyze the rearing-environment enrichment on broiler movement as a function of comfort temperature and heat stress. The results indicated that the simultaneous application of cluster and unrest indexes could monitor the movement of the group of broilers under different environmental conditions. Future monitoring and alert systems based on computer vision should consider the complexity of the environment for detecting heat stress in broiler production.Computer-vision systems for herd detection and monitoring are increasingly present in precision livestock. This technology provides insights into how environmental variations affect the group’s movement pattern. We hypothesize that the cluster and unrest indexes based on computer vision (CV) can simultaneously assess the movement variation of reared broilers under different environmental conditions. The present study is a proof of principle and was carried out with twenty broilers (commercial strain Cobb®), housed in a controlled-environment chamber. The birds were divided into two groups, one housed in an enriched environment and the control. Both groups were subjected to thermal comfort conditions and heat stress. Image analysis of individual or group behavior is the basis for generating animal-monitoring indexes, capable of creating real-time alert systems, predicting welfare, health, environment, and production status. The results obtained in the experiment in a controlled environment allowed the validation of the simultaneous application of cluster and unrest indexes by monitoring the movement of the group of broilers under different environmental conditions. Observational results also suggest that research in more significant proportions should be carried out to evaluate the potential positive impact of environmental enrichment in poultry production. The complexity of the environment is a factor to be considered in creating alert systems for detecting heat stress in broiler production. In large groups, birds’ movement and grouping patterns may differ; therefore, the CV system and indices will need to be recalibrated.

Associations between Sociodemographic Factors, Lifestyle Behaviors, Pregnancy-Related Determinants, and Mediterranean Diet Adherence among Pregnant Women: The GESTAFIT Project.

We examined sociodemographic factors, lifestyle behaviors, and pregnancy-related determinants associated with adherence to the Mediterranean diet (MD) during pregnancy. A total of 152 Caucasian pregnant women were included in this cross-sectional study. Dietary habits and MD adherence were assessed with a food frequency questionnaire. Physical activity (PA) levels and physical fitness (PF) components (cardiorespiratory fitness, relative muscle strength, and flexibility) were objectively measured. A clustered overall PF index was calculated. Participants with a high MD adherence were older, had a lower body mass index (BMI), spent more time in moderate–vigorous PA, had a greater overall PF, cardiorespiratory fitness, and relative muscle strength compared to participants with low MD adherence (all, p < 0.05). When we explored factors associated with improved MD adherence with logistic regression analysis, we found that the following factors: lower pre-pregnancy BMI (OR = 2.337; p = 0.026), meeting PA recommendations (OR = 2.377; p = 0.045), higher relative muscle strength (OR = 2.265; p = 0.016), and higher overall PF (OR = 5.202; p = 0.004) increased the chances to adhere to the MD. Older age, lower BMI, greater PF, and meeting PA recommendations were associated with higher MD adherence. These factors should be considered for a better design of educational programs and guidelines focused on improving materno–fetal health status during pregnancy.

Mapping Spatial Variation and Impact of the National MDA Program on Lymphatic Filariasis Elimination in Ghana: An Initial Study

Lymphatic filariasis (LF) is a public health menace, especially in developing countries. A periodic review of mass drug administration (MDA) performance is critical to monitoring elimination progress. However, investigating the spatial pattern of LF with respect to MDA intervention is yet to be documented. This is essential to appreciating the transmission dynamics across LF-endemic communities and how it is spatially impacted by MDA programs. The aim of this study was to map and explore the spatial variation and hotspots of LF infection among endemic communities and evaluate the impact of the MDA intervention program on its spatial pattern in Ghana. Relative risks, clustering and clusters, prevalence odds ratios, and their confidence intervals were studied with community-level LF data prior to intervention and post intervention periods. The overall risk of LF infection was 0.12% and 0.02% before and after MDA, respectively, suggesting reduced transmission. Using empirical Bayesian smoothing to map the relative risk, a substantial variation in the spatial distribution of the relative risk of LF among endemic communities was observed. Most of the excess prevalence communities were unexpectedly visible even after years of MDA. The Empirical Bayesian Moran’s Index for global clustering showed a reduction in clustering of LF prevalence after MDA with IM = 0.455 and 0.119 for before and after MDA, respectively. Furthermore, examining risks associated with ecological zones, it was observed that the Guinea Savannah and the Transition Zone were the most vulnerable zones for LF infection with prevalence odds ratios 18.70- and 13.20-fold higher than in the reference Moist evergreen zone, respectively. We observed a drastic reduction in risk in the Wet evergreen zone after MDA, while the Guinea Savannah sustained high levels of risk even after MDA. These findings should prompt public health officials to adopt stratified cluster sampling in LF-endemic regions to monitor the rate and density of microfilaria.

Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

AbstractIn recent years, the large amount of heterogeneous data generated by the Internet of Things (IoT) sensors and devices made recording and research tasks much more difficult, and most of the state‐of‐the‐art methods have failed to deal with the new IoT requirements. This article proposes a new efficient method that simplifies data indexing and enhances the quality and velocity of the similarity query search in the IoT environment. In this method, the fog layer was divided into two levels. In the clustering fog level, the incremental density‐based spatial clustering of applications with noise (DBSCAN) algorithm was used to separate collected data into clusters in order to minimize data overlap during in parallel indexes construction. Parallelism was also used, in the indexing fog level to speed up the similarity‐based search process and speed up the similarity‐based search process. The data in each cluster were indexed using our proposed structure called B3CF‐tree (binary tree based on containers at the cloud‐clusters fog computing level). The objects in the leaf nodes of the B3CF‐trees are, finally, stored in the cloud. Using this approach for computing multiple datasets, the retrieve time of the similarity search is significantly reduced. The experimental results showed that the combination of DBSCAN clustering and parallel indexing make the B3CF‐trees outperform the latest real data indexing methods. For example, in terms of quality, the B3CF‐tree has the smallest number of nodes and leaf nodes. In addition, the use of parallelism during kNN search reduced, significantly, the retrieve time of the similarity query search.

Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

In hyperspectral remote sensing, the clustering technique is an important issue of concern. Affinity propagation is a widely used clustering algorithm. However, the complex structure of the hyperspectral image (HSI) dataset presents challenge for the application of affinity propagation. In this paper, an improved version of affinity propagation based on complex wavelet structural similarity index and local outlier factor is proposed specifically for the HSI dataset. In the proposed algorithm, the complex wavelet structural similarity index is used to calculate the spatial similarity of HSI pixels. Meanwhile, the calculation strategy of the spatial similarity is simplified to reduce the computational complexity. The spatial similarity and the traditional spectral similarity of the HSI pixels jointly constitute the similarity matrix of affinity propagation. Furthermore, the local outlier factors are applied as weights to revise the original exemplar preferences of the affinity propagation. Finally, the modified similarity matrix and exemplar preferences are applied, and the clustering index is obtained by the traditional affinity propagation. Extensive experiments were conducted on three HSI datasets, and the results demonstrate that the proposed method can improve the performance of the traditional affinity propagation and provide competitive clustering results among the competitors.

A method of band selection of remote sensing image based on clustering and intra-class index

A method of band selection of remote sensing image based on clustering and intra-class index

Cluster Index Modulation for mmWave Communication Systems

In this study, a novel cluster index modulation (CIM) scheme, which is based on indexing the available clusters in the environment, is proposed for future mmWave communication systems. Exploiting the fact that the available clusters in the system are well separated in terms of their angular distribution, we selected the best path for each of them and then performed IM in an algorithmic manner to convey information bits. It is shown that by means of large antenna arrays and analog RF beamforming with the indexed clusters, the destructive effect of inter-beam/cluster interference can be remarkably mitigated. Also, we designed a hybrid beamforming architecture at the transmitter to further reduce the effect of residual inter-beam/cluster interference, where the analog RF beamformer is followed by a digital baseband precoder using the zero-forcing technique. Computer simulations reveal that the proposed scheme can provide better error performance than traditional mmWave communication, and the proposed hybrid architecture outperforms beam index modulation (BIM) for a point-to-point scenario. Semi-analytical derivations and closed-form unconditional pairwise error probability (UPEP) expressions are derived for both analog and hybrid architectures, which confirm the validity and superiority of our proposed scheme.

Correction to: Effect of Coatings and Coating Methods on Cluster Index in Iron Oxide Pellets for Direct Reduction Shaft Furnaces

Correction to: Effect of Coatings and Coating Methods on Cluster Index in Iron Oxide Pellets for Direct Reduction Shaft Furnaces

Consensus graph and spectral representation for one-step multi-view kernel based clustering

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches.

Subjective clustering in patients with fronto-temporal dementia

In the behavioral variant of frontotemporal dementia (bvFTD) memory deficits have been traditionally considered as due to difficulties in encoding/retrieval frontal strategies. However, the frontal origin of memory deficits in bvFTD has been questioned and hippocampal dysfunction has been also proposed. Here we analyzed bvFTD patients’ proficiency in subjectively organizing memories without an external criterion. Twenty bvFTD patients and 20 healthy individuals were assessed with memory and executive tasks. The ability to subjectively organize memories in the immediate recall of a 15 unrelated word list was measured by calculating the index of subjective clustering (ISC) based on the constancies in response order across the five consecutive free recall trials. Results revealed reduced ISC in bvFTD patients with respect to normal controls. In the bvFTD group, the ISC score correlated with the Corsi span backward score and the number of categories achieved on the Modified Card Sorting Test. The bvFTD patients’ reduced ISC and its correlation with executive performance suggest that executive deficits underlie their defective strategic organization of memories. However, as ISC did not predict memory accuracy in these patients, the memory deficit may not be the mere expression of their executive difficulties.

Estimation of cluster functionals for regularly varying time series: Runs estimators

Cluster indices describe extremal behaviour of stationary time series. We consider runs estimators of cluster indices. Using a modern theory of multivariate, regularly varying time series, we obtain central limit theorems under conditions that can be easily verified for a large class of models. In particular, we show that blocks and runs estimators have the same limiting variance.

鹞落坪国家级自然保护区草本植物多样性及空间分布

鹞落坪国家级自然保护区草本植物多样性及空间分布

Research on food safety prediction method based on k-means clustering algorithm

Aiming at the problem of food risk prediction, this paper proposes a method based on clustering algorithm to predict product risk by raw material risk. Firstly, based on the provincial supply chain closed-loop hypothesis, this paper proposes the selection method of clustering indexes for products and their raw materials. Secondly, this paper uses the k-means clustering algorithm to cluster the products and the corresponding raw materials respectively, then based on the clustering Class results automatically determine the high-risk categories of the products and their raw materials. Finally, the analysis of the experimental data of the 8 categories of products and their raw materials shows that the ratio of the high-risk categories of products and the ratios of the corresponding high-risk categories of raw materials have a strong positive correlation. The experimental results prove the rationality of the raw material clustering index selection method proposed in this paper and the correctness of the method of predicting product risk based on the raw material risk based on the clustering algorithm.