Rough Clusters Research Articles

A robust multi-view knowledge transfer-based rough fuzzy C-means clustering algorithm

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images.

Zanthoxylum infructescence detection based on adaptive density clustering

Infructescence detection during the early fruiting stage is a necessary preliminary work to estimate the yield of Zanthoxylum. The purpose of this research is to detect and quantify the infructescences on the images of early fruit-bearing branches of Zanthoxylum which are collected in the natural environment. Thus, a machine vision-based algorithm for detecting Zanthoxylum infructescences is proposed, which contains of two phases. The first is to segment fruit-like and non-fruit region from Zanthoxylum branch image by extracting the Histogram of Oriented Gradient (HOG) feature map and Excess Green minus Excess Red (ExGR) index to obtain the fruits of Zanthoxylum infructescences. The second is to identify fruits adaptively and roughly in accordance with the density of their position distribution, then an optimization model is established to merge rough clusters and the optimal clustering result is obtained. Experiments with normal samples demonstrate that the proposed approach receives a Precision of 96.67%, a Recall of 91.07%, and an F1-score of 0.93. Compared with ADPC-kNN, DBSCAN and OPTICS, the results reveal that the proposed algorithm has an improved robustness and achieves higher Recall and F1-score. Meanwhile, its competitiveness is displayed in the experiments with deep learning-based methods. It can adaptively detect the infructescences of Zanthoxylum branch images, and the experiments prove that it is effective.

Čech L-Fuzzy Rough Proximity Spaces

In order to define the concept of nearness between [Formula: see text]-fuzzy rough sets, we introduce the concept of Čech [Formula: see text]-fuzzy rough proximity spaces. On this new nearness structure, we prove some basic topological results. To support the proposed approach, examples are given. Further, we introduce [Formula: see text]-fuzzy rough grills, [Formula: see text]-fuzzy rough filters and [Formula: see text]-fuzzy rough clusters and on Čech [Formula: see text]-fuzzy rough proximity spaces, and find the relationship between them.

Particle Competitive Mechanism Based Multiobjective Rough Clustering Algorithm for Image Segmentation

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper and lower approximations of rough clusters, which may bring a great effect on the clustering performance. When applied to image segmentation, rough clustering is always sensitive to the initialized cluster centers and image noise. Furthermore, only one clustering criterion is considered in rough clustering, which cannot satisfy diverse practical requirements. To handle these issues, a particle competitive mechanism based multiobjective rough clustering algorithm (PCM-MORCA) for image segmentation is proposed. First, a rough intraclass compactness function considering the nonlocal spatial information derived from an image is constructed to overcome the sensitivity to image noise. Next, the constructed rough intraclass compactness function and an interclass separation function are optimized simultaneously to make cluster centers meet diverse segmentation requirements. Then, an adaptive threshold determination mechanism by which the threshold adaptively varies with the clustered data is presented to well determine the upper and lower approximations of rough clusters. After that, to effectively search appropriate cluster centers, a novel pair competition-based particle weight updating strategy is designed for multiobjective particle swarm optimization by improving the elite particle selection and particle update. Finally, a rough clustering index with the nonlocal spatial information is constructed for selecting the optimal solution for PCM-MORCA. Segmentation experiments on Berkeley and magnetic resonance images reveal that PCM-MORCA behaves well on the segmentation accuracy and noise robustness.

Effect of <i>Ginkgo biloba</i> extract on Histomorphometric changes of Hippocampus against Fluoride Toxicity in Wistar Rats

Prolonged ingestion of fluoride leads to the pathogenesis known as fluorosis. Fluoride exposure leads to neuro degenerative changes such as reduction in the neuronal cell size and number. Exposure to fluoride showed neurodegenerative changes like shrunken neurons, increased in folding of the nuclear membrane, mitochondrial alterations, and dilated rough endoplasmic reticulum cisternae and clusters of vesicles near the Golgi bodies. Present study is to explore the histomorphometric changes of the hippocampus of fluoride induced brain and treated with Ginkgo biloba Extract (GBE). Thirty number (30) adult male Wistar rats were randomly separated into 5 Groups (n=6). Group1 (Control) supplemented with water, 2 to 5 Groups were supplemented with 100ppm of sodium fluoride for 30 days, while the Groups 3, 4, and 5 were Ginkgo biloba extract treated at 50mg/ kg, 100mg/kg and 200mg/kg body weight for 15 days, after sodium fluoride treatment, results showed that, 200mg/kg GBE provided effective and complete protection against fluoride toxicity. From the present study concluded that 50, 100 and 200 mg/kg GBE was significantly attenuated the fluoride induced toxicity in a dose depending manner. Present histomorphometrical study showed delayed neuronal death in the CA1 and CA3 regions of hippocampus of fluoride group that was significantly attenuated by GBE (50,100 and 200 mg/kg) in a dose dependant manner.

Design Analysis of Approximate Redundant Binary Multipliers

As innovation scaling is arriving at its cutoff points, new methodologies have been proposed for computational effectiveness. Inexact processing is a promising method for elite execution and low power circuits as utilized in blunder lenient applications. Among surmised circuits, rough number juggling plans have drawn in huge exploration interest. In this paper, the plan of rough excess twofold (RB) multipliers is contemplated. Two inexact Booth encoders and two RB 4:2 blowers dependent on RB (full and half) adders are proposed for the RB multipliers. The inexact plan of the RB-Normal Binary (NB) converter in the RB multiplier is additionally concentrated by considering the mistake qualities of both the estimated Booth encoders and the RB blowers. Both rough and accurate ordinary incomplete item clusters are utilized in the surmised RB multipliers to meet distinctive exactness prerequisites. Mistake investigation and equipment reenactment results are given. The proposed surmised RB multipliers are contrasted and past rough Booth multipliers; the outcomes show that the estimated RB multipliers are superior to inexact NB Booth multipliers particularly when the word size is huge. Contextual investigations of blunder tough applications are likewise introduced to show the legitimacy of the proposed plans.

Medical consumable usage control based on Canopy_K-means clustering and WARM

Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.

Automatic short answer grading using rough concept clusters

Evaluation of text-based answers has stayed as a challenge for researchers in recent years and with the growing acceptance of e-learning system, a solution needs to be achieved fast. While assessin...

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams.

A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.

Joint Clusters and Iterative Graph Cuts for ALS Point Cloud Filtering

A novel method that combines joint clusters and iterative graph cuts for ALS point cloud filtering is proposed in this paper. The method first extracts clusters of points from the raw point cloud, and then classifies ground points at the cluster level. There are four main steps, i.e., two-step point cloud clustering, critical point extraction, initial terrain determination, and terrain densification based on iterative graph cuts. Smooth clusters, rough clusters, and scattered points are extracted by the two-step clustering to depict the raw point cloud, which reduces the complexity of raw data and the judgment difficulty in the subsequent procedures. Critical points of each cluster are extracted, and the initial terrain is determined among the smooth clusters. Using the initial terrain and critical points, iterative graph cuts is performed to segment ground and nonground points at the cluster level. Experiments on ISPRS dataset with a low point density and Utah dataset with a moderate point density show that our approach provides a satisfactory trade off between Type I and Type II errors. Moreover, our method significantly outperforms progressive TIN densification based filters, and successfully controls Type II errors.

No distinct barrier effects of highways and a wide river on the genetic structure of the Alpine newt (Ichthyosaura alpestris) in densely settled landscapes

Linear landscape elements such as roads, railways and rivers have been shown to act as barriers to dispersal and gene flow, hence impeding functional connectivity and increasing genetic differentiation between individuals or populations on opposite sides of the barrier. Such putative barriers act through a confluence of mechanisms, including crossing mortality, barrier avoidance and modifications to organisms’ effective dispersal patterns. Small, terrestrial animals such as amphibians are predicted to be vulnerable to the effects of such barriers given their limited locomotive performance and their dependence on spatially distinct breeding habitats. Here, we examined the effects of highways and a wide river on Ichthyosaura alpestris in three regions of northern Switzerland by measuring the genetic differentiation between local populations and describing the spatial genetic structure. Moreover, we estimated effective population sizes as an indicator for the susceptibility of populations to random genetic drift. Based on genetic differentiation, we found evidence to suggest that the highways and river acted as barriers to gene flow for the newt in the study regions, but results were inconsistent when ignoring breeding ponds with low samples sizes. Admixture-based genetic clustering suggested the delineation of the genotypes to rough regional clusters, with only weak structure inferred within these clusters. Thus, results suggest that at present, highways and rivers do not substantially affect the genetic structure of I. alpestris within northern Switzerland in a negative manner. Alternatively, the lack of a distinct genetic structure in regional newt populations may be explained by, e.g., large effective population sizes.

Predicting Diabetics Accuracy Using Rough Set Clusters

Predicting Diabetics Accuracy Using Rough Set Clusters

Hydrogen sensing properties of Pt/Pd bimetal decorated on highly hydrophobic Si nanowires

This paper describes the synthesis of Pt/Pd bimetal decorated highly hydrophobic rough silicon nanowires (Si NWs) by using a facile metal assisted chemical etching and pulse laser deposition (PLD) process. In addition, the potential of the Si NWs as a new hydrogen (H2) detection matrix was investigated. The Pt/Pd bimetal was coated as a discrete ultra-thin film manner in a semi-dense configuration over the basal podium and along the length of vertical-standing and semi-densely distributed rough Si NW clusters. The resulting structure showed significant advantages in H2 sensing performances such as a large detection range of 1–40,000 ppm, high response magnitude of nearly 5.02%, and fast response-recovery time of 7/7 s to 10,000 ppm (1 vol%) hydrogen concentration at an optimum operating temperature of 75 °C. The observed characteristics of fast response-recovery time could relate to the enhanced hydrogen-induced changes in the work function of the Pt/Pd bimetal decorated Si NWs (Pt/Pd–Si NWs), which were configured as an array matrix comprising of many nanowire clusters with variable distances between the two electrodes. Finally, the fabricated sensor showed excellent repeatability towards (1 vol%) hydrogen concentration.

Ordered Self-Similar Patterns in Anisotropic Stochastic Growth.

We propose an anisotropic stochastic growth model to rationalize the anisotropic self-assembly of supramolecules to form elongated two-dimensional ribbon structures in a recent experiment. The model exhibits distinct growth scenarios that are critically controlled by the ratio of the transverse and the longitudinal growth rate. In the regime of suppressed transverse growth, the model generates the experimentally observed elongated structures through layer-by-layer growing. We further observe the convergence of rough clusters toward smooth regular elliptic patterns by averaging over a number of independent growth processes. Remarkably, these resulting elliptic clusters are self-similar in each instantaneous moment in the growth process. Statistical analysis suggests that the realization of such ordered patterns does not rely on the delicate coordination of different parts in the cluster growth. The self-similarity phenomenon derived from this idealized model may have wider implications, notably in the designed clustering of various elementary building blocks with anisotropic interactions.

Fabrication of Hydrophobic Surface on Wood Veneer via Electroless Nickel Plating Combined with Chemical Corrosion

Birch veneers were coated with Ni-P films by a combined process of KBH4 activation and electroless plating. The plated veneers were further chemically corroded to obtain hydrophobic surfaces on wood. The effect of chemical corrosion on the contact angle of the veneers was investigated. The hydrophobic veneers were characterized by X-ray photo electron spectroscopy (XPS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The surface contact angle of birch veneer before and after it was plated with Ni-P alloy coating was 41º and 121º, respectively. The contact angle reached 136.7º when the nickel-coated veneers were corroded in CuSO4 aqueous solution for 30 min. XPS analysis showed that Cu0 cluster doped with little CuO formed on the corroded surface of Ni-P alloy film after chemical corrosion. SEM and XRD showed that rough copper clusters formed on the surface of the wood veneer and revealed the reason of the surface hydrophobicity. This study provides a new pathway for fabricating hydrophobic wood.

Influence of carbon vacancies on the adsorption of Au on TiC(001): a first-principles study

In this work, it is found that the adsorption sites and geometries of Au clusters on TiC(001) surfaces containing carbon vacancies are significantly different from that without vacancies. In TiC(001) with carbon vacancies, the most preferential adsorption sites for Au are on top of vacancies sites, and then the sites neighboring the vacancies. The tendency for 3D Au clusters formation in TiC(001) with carbon vacancies is weaker and Au atoms are more likely to form rough 2D clusters. Furthermore, more charge is transferred from the substrate to Au and the polarization of electrons is stronger. In addition, the diffusion of Au on TiC(001) surface containing carbon vacancies is more difficult. The above affects can be helpful for improving the chemical activation of Au/TiC surfaces. Therefore, introducing carbon vacancies in the surfaces should be a very promising method to enhance the activation of Au/TiC systems.

A fast method based on multiple clustering for name disambiguation in bibliographic citations

Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse‐to‐fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state‐of‐the‐art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.

Rough ISODATA Algorithm

Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering. Even though k-means clustering is a popular clustering method widely used, its application requires the knowledge of the number of clusters present in the given data set. Several solutions are available in literature to overcome this limitation. The k-means clustering method creates a disjoint and exhaustive partition of the data set. However, in some situations one can come across objects that belong to more than one cluster. In this paper, a clustering algorithm capable of producing rough clusters automatically without requiring the user to give as input the number of clusters to be produced. The efficiency of the algorithm in detecting the number of clusters present in the data set has been studied with the help of some real life data sets. Further, a nonparametric statistical analysis on the results of the experimental study has been carried out in order to analyze the efficiency of the proposed algorithm in automatic detection of the number of clusters in the data set with the help of rough version of Davies-Bouldin index.

A novel ant-based clustering algorithm using Renyi entropy

Ant-based clustering is a type of clustering algorithm that imitates the behavior of ants. To improve the efficiency, increase the adaptability to non-Gaussian datasets and simplify the parameters of the algorithm, a novel ant-based clustering algorithm using Renyi Entropy (NAC-RE) is proposed. There are two aspects to application of Renyi entropy. Firstly, Kernel Entropy Component Analysis (KECA) is applied to modify the random projection of objects when the algorithm is run initially. This projection can create rough clusters and improve the algorithm's efficiency. Secondly, a novel ant movement model governed by Renyi entropy is proposed. The model takes each object as an ant. When the object (ant) moves to a new region, the Renyi entropy in its local neighborhood will be changed. The differential value of entropy governs whether the object should move or be moveless. The new model avoids complex parameters that have influence on the clustering results. The theoretical analysis has been conducted by kernel method to show that Renyi entropy metric is feasible and superior to distance metric. The novel algorithm was compared with other classic ones by several well-known benchmark datasets. The Friedman test with the corresponding Nemenyi test are applied to compare and conclude the algorithms’ performance The results indicate that NAC-RE can get better results for non-linearly separable datasets while its parameters are simple.

Silica Nanohybrid Membranes with High CO2 Affinity for Green Hydrogen Purification

AbstractAn effective separation of CO2 from H2 can be achieved using currently known polyethylene oxide (PEO)‐based membranes at low temperatures but the CO2 permeability is inadequate for commerical operations. For commercial‐scale CO2/H2 separation, CO2 permeability of these membranes must be significantly enhanced without compromising CO2/H2 selectivity. We report here exceptional CO2/H2 separation properties of a nanohybrid membrane comprising polyethylene glycol methacrylate (PEGMA) grafts on an organic‐inorganic membrane (OIM) consisting of a low molecular weight polypropylene oxide (PPO)‐PEO‐PPO diamine and 3‐glycidyloxypropyltrimethoxysilane (GOTMS), an alkoxysilane. The CO2 gas permeability of this nanohybrid membrane can reach 1990 Barrer with a CO2/H2 selectivity of 11 at 35 °C for a mixed gas mixture comprising 50% CO2 ‐ 50% H2 at 3.5 atm. The transformation of the inorganic silica phase from a well‐dispersed network of finely defined nanoparticles to rough porous clusters appears to be responsible for this OIM membrane exceeding the performance of other state‐of‐the‐art PEO‐based membranes.