Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

With the rapid development of the economy,the scale of the power grid is expanding.The number of power equipment that constitutes the power grid has been very large,which makes the state data of power equipment grow explosively.These multi-source heterogeneous data have data differences,which lead to data variation in the process of transmission and preservation,thus forming the bad information of incomplete data.Therefore,the research on data integrity has become an urgent task.This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system.According to the characteristics and data sources of the massive data generated by power equipment,the fuzzy mining model of power equipment data is established,and the data is divided into numerical and non-numerical data based on numerical data.Take the text data of power equipment defects as the mining material.Then,the Apriori algorithm based on an array is used to mine deeply.The strong association rules in incomplete data of power equipment are obtained and analyzed.From the change trend of NRMSE metrics and classification accuracy,most of the filling methods combined with the two frameworks in this method usually show a relatively stable filling trend,and will not fluctuate greatly with the growth of the missing rate.The experimental results show that the proposed algorithm model can effectively improve the filling effect of the existing filling methods on most data sets,and the filling effect fluctuates greatly with the increase of the missing rate,that is,with the increase of the missing rate,the improvement effect of the model for the existing filling methods is higher than 4.3%.Through the incomplete data clustering technology studied in this paper,a more innovative state assessment of smart grid reliability operation is carried out,which has good research value and reference significance.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

Application of FCM Algorithm Combined with Artificial Neural Network in TBM Operation Data

Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine.However,the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional data.We propose a new Fuzzy clustering algorithm,namely FCM-ANN algorithm.The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the artificial neural network.This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion,but also can effectively cluster the functional data.In this paper,we first use the t-test as an evaluation index and apply the FCM-ANN algorithm to the synthetic datasets for validity testing.Then the algorithm is applied to TBM operation data and combined with the crossvalidation method to predict the tunneling speed.The predicted results are evaluated by RMSE and R^(2).According to the experimental results on the synthetic datasets,we obtain the relationship among the membership threshold,the number of samples,the number of attributes and the noise.Accordingly,the datasets can be effectively adjusted.Applying the FCM-ANN algorithm to the TBM operation data can accurately predict the tunneling speed.The FCM-ANN algorithm has improved the traditional fuzzy clustering algorithm,which can be used not only for the prediction of tunneling speed of TBM but also for clustering or prediction of other functional data.

Architecture of Integrated Data Clustering Machine

Data clustering is a significant information retrieval technique in today＇s data intensive society. Over the last few decades a vast variety of huge number of data clustering algorithms have been designed and implemented for all most all data types. The quality of results of cluster analysis mainly depends on the clustering algorithm used in the analysis. Architecture of a versatile, less user dependent, dynamic and scalable data clustering machine is presented. The machine selects for analysis, the best available data clustering algorithm on the basis of the credentials of the data and previously used domain knowledge. The domain knowledge is updated on completion of each session of data analysis.

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

Profiling Astronomical Objects Using Unsupervised Learning Approach

Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields.Instead of a manual inspection,various automated systems are invented to satisfy the need,including the classification of light curve profiles.A specific Kaggle competition,namely Photometric LSST Astronomical Time-Series Classification Challenge(PLAsTiCC),is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope(LSST)project.Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types.As this challenge focuses on developing a predictive model that is robust to classifying unseen data,those previous attempts similarly encounter the lack of discriminate features,since distribution of training and actual test datasets are largely different.As a result,well-known classification algorithms prove to be sub-optimal,while more complicated feature extraction techniques may help to slightly boost the predictive performance.Given such a burden,this research is set to explore an unsupervised alternative to the difficult quest,where common classifiers fail to reach the 50%accuracy mark.A clustering technique is exploited to transform the space of training data,from which a more accurate classifier can be built.In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning,a multiple-clustering alternative is also introduced with improved performance.In fact,it is able to yield a higher accuracy rate of 58.32%from 51.36%that is obtained using a simple clustering.For this difficult problem,it is rather good considering for those achieved by well-known models like support vector machine(SVM)with 51.80%and Naive Bayes(NB)with only 2.92%.

A Novel Density-Based Spatial Clustering of Application with Noise Method for Data Clustering

The traditional methods are easy to generate a large number of fake samples or data loss when classifying unbalanced data.Therefore,this paper proposes a novel DBSCAN(density-based spatial clustering of application with noise)for data clustering.The density-based DBSCAN clustering decomposition algorithm is applied to most classes of unbalanced data sets,which reduces the advantage of most class samples without data loss.The algorithm uses different distance measurements for disordered and ordered classification data,and assigns corresponding weights with average entropy.The experimental results show that the new algorithm has better clustering effect than other advanced clustering algorithms on both artificial and real data sets.

Robust Regression Analysis for Clustered Interval-Censored Failure Time Data

Clustered interval-censored failure time data often occur in a wide variety of research and application fields such as cancer and AIDS studies. For such data, the failure times of interest are interval-censored and may be correlated for subjects coming from the same cluster. This paper presents a robust semiparametric transformation mixed effect models to analyze such data and use a U-statistic based on rank correlation to estimate the unknown parameters. The large sample properties of the estimator are also established. In addition, the authors illustrate the performance of the proposed estimate with extensive simulations and two real data examples.

Nonparametric Estimation in Linear Mixed Models with Uncorrelated Homoscedastic Errors

Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component in which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao’s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

Autonomous Clustering Using Rough Set Theory

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

COOPERATIVE CLUSTERING BASED ON GRID AND DENSITY

Based on the analysis of features of the grid-based clustering method-clustering in quest （CLIQUE） and density-based clustering method-density-based spatial clustering of applications with noise （DBSCAN）, a new clustering algorithm named cooperative clustering based on grid and density （CLGRID） is presented. The new algorithm adopts an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGRID on test data sets demonstrates its validity and higher efficiency, which contrast with tradi- tional DBSCAN with R tree.

A Semiparametric Additive Rates Model for Clustered Recurrent Event Data

Recurrent event data often arises in biomedical studies, and individuals within a cluster might not be independent. We propose a semiparametric additive rates model for clustered recurrent event data, wherein the covariates are assumed to add to the unspecified baseline rate. For the inference on the model parameters, estimating equation approaches are developed, and both large and finite sample properties of the proposed estimators are established.

Modeling the Evolution of Chorus Waves into Hiss Waves in the Magnetosphere

In this study, we analyze Cluster observations of whistler-mode chorus and hiss waves during the event of August 19-21, 2006. Chorus is present outside the plasmasphere and hiss occurs inside the plasmasphere. Using a recently constructed plasma boundary layer model, we perform a ray-tracing study on the propagation of chorus. Numerical results show that chorus can penetrate into the plasmasphere through the plasma boundary layer, evolving into hiss. The current data analysis and modeling provide a further observational support for the previous findings that chorus is the origin of plasmaspheric hiss.

A Distributed Dynamic Clustering Algorithm for Wireless Sensor Networks

This paper proposes a distributed dynamic k-medoid clustering algorithm for wireless sensor networks （WSNs）, DDKCAWSN. Different from node-clustering algorithms and protocols for WSNs, the algorithm focuses on clustering data in the network. By sending the sink clustered data instead of practical ones, the algorithm can greatly reduce the size and the time of data communication, and further save the energy of the nodes in the network and prolong the system lifetime. Moreover, the algorithm improves the accuracy of the clustered data dynamically by updating the clusters periodically such as each day. Simulation results demonstrate the effectiveness of our approach for different metrics.

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume,analyzing and retrieving this vast data volume is much more essential than ever.One of the main techniques that would be beneficial in this regard is called the Clustering method.Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible.One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm.The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters.Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time.Besides,the selection of appropriate initial seeds can reduce the cluster’s inconsistency.In this paper,we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm.For this purpose,a new method is proposed considering the average distance between objects to determine the initial seeds.Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm.The experimental results showed that our proposed approach outperforms the Chithra with 1.7%and 2.1%in terms of clustering accuracy for Wine and Abalone detection data,respectively.Furthermore,achieved results indicate that comparing with the Reverse Nearest Neighbor(RNN)search approach,the proposed method has a higher convergence speed.

Fighting against COVID-19: Who Failed and Who Succeeded?

Recently, governments and public authorities in most countries had to face the outbreak of COVID-19 by adopting a set of policies. Consequently, some countries have succeeded in minimizing the number of confirmed cases while the outbreak in other countries has led to their healthcare systems breakdown. In this work, we introduce an efficient framework called COMAP (COrona MAP), aiming to study and predict the behavior of COVID-19 based on deep learning techniques. COMAP consists of two stages: clustering and prediction. The first stage proposes a new algorithm called Co-means, allowing to group countries having similar behavior of COVID-19 into clusters. The second stage predicts the outbreak’s growth by introducing two adopted versions of LSTM and Prophet applied at country and continent scales. The simulations conducted on the data collected by WHO demonstrated the efficiency of COMAP in terms of returning accurate clustering and predictions.

CRISE: Toward Better Understanding of COVID-19 Psychological Impact during Lockdown

Recently, the COVID-19 emerged in China and propagated around all the world has threatened millions of people and affected most countries and governments at several sides such as economical, educational, tourism, healthcare, etc. Indeed, one of the most important challenges that directly affect the people is the psychological side due to the harsh policies imposed by public authorities in most countries. In this paper, we propose a framework called CRISE that allows studying and understanding the psychological effect of COVID-19 during the lockdown period. Mainly, CRISE consists of four data stages: Collection, tRansformation, reductIon, and cluStEring. The first stage collects data from more than 2000 participants through a questionnaire containing attributes related to psychological effect before and during the lockdown. The second stage aims to preprocess the data before performing the study stage. The third stage proposes a model that finds the similarities among the attributes, based on the correlation matrix, to reduce its number. Finally, the fourth stage introduces a new version of Kmeans algorithm, called as Jaccard-based Kmeans (JKmeans), that allows to group participants having similar psychological situation in the same cluster for a later analysis. We show the effectiveness of CRISE in terms of clustering accuracy and understanding the psychological effect of COVID-19.