Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis

BackgroundCluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing “outliers” among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges.ResultsWe present a new approach, implemented in an R package called Thresher, to cluster objects in general datasets. Thresher combines ideas from principal component analysis, outlier filtering, and von Mises-Fisher mixture models in order to select the optimal number of clusters. We performed a large Monte Carlo simulation study to compare Thresher with other methods for detecting outliers and determining the number of clusters. We found that Thresher had good sensitivity and specificity for detecting and removing outliers. We also found that Thresher is the best method for estimating the optimal number of clusters when the number of objects being clustered is smaller than the number of variables used for clustering. Finally, we applied Thresher and eleven other methods to 25 sets of breast cancer data downloaded from the Gene Expression Omnibus; only Thresher consistently estimated the number of clusters to lie in the range of 4–7 that is consistent with the literature.ConclusionsThresher is effective at automatically detecting and removing outliers. By thus cleaning the data, it produces better estimates of the optimal number of clusters when there are more variables than objects. When we applied Thresher to a variety of breast cancer datasets, it produced estimates that were both self-consistent and consistent with the literature. We expect Thresher to be useful for studying a wide variety of biological datasets.

Classifying organometallic reactions into distinct reaction types is fundamentally important for understanding mechanisms and predicting reactions, for synthesis optimisation. Fundamental to classification of organometal reactions is reaction representation, but current methods often fail to capture organometal chemical transformation adequately. The study has adopted a hybrid fingerprinting approach, whereby new fingerprints were concatenated with permutation important Morgan fingerprints to create 49 to 63 bits fingerprints. The hybrid fingerprints were used to build KMeans clustering, Guassian mixtures, Affinity propagation and Heirarchical clustering models for classification of organometal reactions. The models successfully classified reactions into 6-8 major organometal reaction types. Of note, the fingerprints consistently outperformed Morgan fingerprints across all clustering models with good Davies–Bouldin Index (DBI) ranging from 0.3 to 0.6 and Silhouette score from 0.3 to 0.8. Furthermore, the clustering models were visualized using Principal Component Analysis. Affinity Propagation and KMeans demonstrated superior performance over Hierarchical and GMM algorithms in distinguishing major reaction categories. In contrast, the Hierarchical model excelled at identifying sub-level classifications compared to the other methods. Consequently, Affinity Propagation and KMeans are recommended for broad reaction type classification, while the Hierarchical approach is better suited for resolving detailed subclass distinctions. The observed variability in model performance further highlighted the importance of feature selection and representation in clustering models. Future studies should look into improving the fingerprints to recognize subcategories.

Introduction: Saffron is one of the most important economic plants in the Khorasan province. Awareness of soil quality in agricultural lands is essential for the best management of lands and for obtaining maximum economic benefit. In general, plant growth is a function of environmental factors especially chemical and physical properties of soil (20). It has been demonstrated that there was a positive and high correlation between soil organic matter and saffron yield. Increasing the yield of saffron due to organic matter is probably due to soil nutrient, especially phosphorous and nitrogen and also improvement of soil physical quality (6, 28, 29). The yield of saffron in soils with high nitrogen as a result of vegetative growth is high (8). Shahandeh (6) found that most of the variation of saffron yield depends on soil properties. Due to the economic importance of saffron and the role of soil properties on saffron yield, this research was conducted to find the relationship between saffron yield and some soil physical and chemical properties, and to determine the contribution of soil properties that have the greatest impact on saffron yield in the Ghayenat area. Materials and Methods: This research was performed in 30 saffron fields (30 soil samples) of the Ghayenat area (longitude 59° 10΄ 10.37˝ - 59° 11΄ 38.41˝ and latitude 33° 43΄ 35.08˝ - 33΄ 44΄ 02.78˝), which is located in the Khrasan province of Iran. In this research, 21 soil properties were regarded as the total data set (TDS). Then the principal component analysis (PCA) was used to determine the most important soil properties affecting saffron yield as a minimum data set (MDS) and the stepwise regression to estimate saffron yield. To estimate the yield of saffron in stepwise regression method, saffron yield was considered as a dependent variable and soil physical and chemical properties were considered to be independent variables. Results and Discussion: According to the PCA method, among the 21 studied properties, 7 out of them including calcium, iron, zinc contents, sand, calcium carbonate equivalent percent, mean weight diameter of aggregates (MWD) and manganese (Mn) had the higher Eigenvalues. Therefore, the above properties were introduced as the most important soil properties in saffron fields. Calcium carbonate had the negative effect on the availability of micro-nutrients (26). Christensen et al. (15) found that by increasing the calcium carbonate in soil due to high pH and formation of insoluble components, the uptake of micro-nutrients is especially limited. The results of stepwise regression method (equation 1) showed that soil acidity (pH), zinc content, bulk density, MWD, iron content, salinity (EC), organic carbon and available potassium in soil were the most important properties that affect the yield of saffron, so that the determination coefficient (R2) of the regression model was high (Table 2) and it can explain 74% of the variation of saffron yield. Y = 6924.51 – 1187.31 pH – 89.65 EC + 71.6 Fe – 826.02 Zn + 471.55 OC, + 5490.96 K + 1353.56 BD + 752.82 MWD (1) where Y: saffron yield (kgha-1), pH: soil acidity, EC: electoral conductivity (dSm-1), Fe: iron concentration (mgkg-1), Zn: zinc concentration (mgkg-1), OC: organic carbon (%), K: soil potassium (%), BD: soil bulk density (Mgm-3), and MWD: mean weight diameter of aggregates (MM). Based on the absolute values of standard s in the regression model (Table 3), pH value and then after Zn concentration had the most effect on saffron yield. In general, responses of different plants to soil pH is varied, and saffron grows satisfactory in pH = 7.8 (5). Soil pH influences the uptake of soil nutrients by plants (15), so that this parameter had the most effect on saffron yield and by increasing the soil pH, the yield of saffron decreases. According to the regression model, Zn concentration was the second parameter in saffron yield. Zn has the important role in structure of plant enzymes (30). After these 2 parameters, Bd, MWD, Fe concentration, EC, Organic carbon and K concentration in soil had more effect on saffron yield (Table 3). Conclusion: According to both PCA and regression methods, the concentration of iron and zinc and MWD were determined as the important and effective soil properties on saffron yield in the Ghayenat area. In addition, soil pH in stepwise regression method and calcium carbonate in PCA method were determined as the effective properties on saffron yield. Therefore, it is suggested that the parameters of Zn, Fe, and MWD along with soil pH and calcium carbonate which were regarded individually in two methods, were considered as the most soil properties in saffron yield.

Mangroves are coastal plants influenced by tidal cycles. One of the regions in South Sumatra Province with a mangrove ecosystem is Sembilang National Park. As a national park, this location is strategically positioned for research related to mangrove species. The utilization of the Principal Component Analysis (PCA) method is considered to enhance the capabilities of remote sensing data in mangrove mapping. However, its application has been limited to the mangrove level. The research objective is to identify mangrove species in Sembilang National Park using the PCA method by leveraging Landsat-8 image data acquired on September 9, 2019. Mangrove distribution is obtained through the Mangrove Vegetation Index (MVI) and vector data from Global Mangrove Watch. The image is then overlaid with field species data to obtain species spectral patterns. Additionally, the correlation between spectral band values and eigenvalues (PC) is analyzed to detect parameters correlated with eigenvalues. The research results show that PC values correlate with mangrove species and can be used for mangrove species identification. This is demonstrated by Bruguiera Gymnorrhiza species with canopy coverage of 60.8% and 62.46% at ST7 and ST8 having the same PC values, while mangroves with canopy coverage of 62.5% in different species have different PC values. Moreover, the PCA method can indicate two crucial factors in identifying mangrove species, namely vegetation and soil factors. Mangrove merupakan tumbuhan pesisir yang dipengaruhi oleh pasang surut. Salah satu wilayah di Provinsi Sumatera Selatan yang memiliki ekosistem mangrove adalah Taman Nasional Sembilang. Sebagai taman nasional, lokasi ini sangat strategis untuk dilakukan penelitian terkait spesies mangrove. Pemanfaatan metode principal component analysis (PCA) dinilai mampu meningkatkan kemampuan data penginderaan jauh dalam pemetaan mangrove. Namun selama ini, pemanfaatan terbatas pada level mangrove. Adapun tujuan penelitian adalah mengidentifikasi spesies mangrove di Taman Nasional Sembilang menggunakan metode PCA dengan memanfaatkan data citra Landsat-8 yang diakusisi pada 09 September 2019. Sebaran mangrove diperoleh melalui indeks vegetasi mangrove (MVI) dan data vektor dari Global Mangrove Watch. Citra tersebut kemudian di overlay dengan data spesies lapangan untuk mendapatkan pola spektral species. Selain itu, korelasi nilai spektral band dan eigen (PC) dianalisis untuk mendeteksi parameter yang berkorelasi dengan nilai eigen. Hasil penelitian menunjukkan nilai PC memiliki korelasi dengan spesies mangrove dan dapat digunakan untuk mengidentifikasi spesies mangrove. Hal ini ditunjukkan spesies Bruguiera Gymnorrhiza dengan tutupan kanopi 60,8% dan 62,46% pada ST7 dan ST8 memiliki nilai PC yang sama serta mangrove dengan besaran tutupan kanopi 62,5% pada spesies nilai PC berbeda. Selain itu, metode PCA mampu menunjukkan dua faktor penting dalam mengidentifikasi spesies mangrove, yaitu faktor vegetasi dan tanah.

The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.

Systematic principal component analysis (PCA) methods are presented in this paper for reliable islanding detection for power systems with significant penetration of distributed generations (DGs), where synchrophasors, recorded by phasor measurement units, are used for system monitoring. Existing islanding detection methods, such as rate-of-change-of-frequency and vector shift are fast for processing local information; however, with the growth in installed capacity of DGs, they suffer from several drawbacks. Incumbent genset islanding detection cannot distinguish a system-wide disturbance from an islanding event, leading to maloperation. The problem is even more significant when the grid does not have sufficient inertia to limit frequency divergences in the system fault/stress due to the high penetration of DGs. To tackle such problems, this paper introduces PCA methods for islanding detection. A simple control chart is established for intuitive visualization of the transients. A recursive PCA scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. To further reduce the computational burden, the approximate linear dependence condition errors are calculated to update the associated PCA model. The proposed PCA and RPCA methods are verified by detecting abnormal transients occurring in the U.K. utility network.

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

ABSTRACTThis study presents an integrated approach, based on data envelopment analysis (DEA) and principal component analysis (PCA) methods, to evaluate the influence of Six Sigma deployment on key job characteristics in an automotive industry. The job characteristics are defined as satisfaction, stress, and security. A standard questionnaire is designed and distributed among the employees at the company's production site, who were affected by the implementation of Six Sigma. DEA and PCA methods are applied to measure the performance of the sub-groups of employees in the company. Consequently, the most efficient and inefficient sub-groups are determined. According to the findings of this investigation, it was perceived that the implementation of Six Sigma has had the greatest impact on job satisfaction. Additionally, a design of experiment was carried out to recognize the most effective job factor, which was identified to be the overall working conditions for the related case study. This is the first study that integrates DEA and PCA toward identifying and optimizing job characteristics in terms of Six Sigma implementation. The approach, employed in this study, can be easily used in the other manufacturing systems, in order to assist them to identify and improve their key job characteristics.

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

Satellite microwave emission mixed with signals from active sensors is referred to as radio-frequency interference (RFI). RFI affects greatly the quality of data and retrieval products from space-borne microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis (PCA) method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index (sensitive to RFI signal) and two scattering indices (sensitive to snow scattering), are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the “true” RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals’ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

With the development of shale gas reservoirs, various fracture propagation models have been developed to predict hydraulic fracture geometry by considering the stress shadow effects. Also, many researchers have been working on development of various production-simulation approaches to simulate production from the complex fracture geometries. The objective of this study is to combine the fracture propagation modeling and reservoir simulation to optimize production in shale gas reservoirs. First, we have developed a semi-analytical model to simulate shale gas production from the complex non-planar fracture geometry with varying fracture width and fracture permeability along fracture length. The important gas transport mechanisms including gas slippage, gas diffusion, and gas desorption are considered. Second, we integrate the semi-analytical model with a three-dimensional fracture propagation model, which fully couples elastic deformation of the rock and fluid flow to simulate complex hydraulic fracture propagation, to optimize fracture treatment design in Marcellus shale gas reservoirs. Specifically, the fracture propagation model is utilized to predict the more-realistic non-planar fracture geometry with varying fracture width and fracture permeability along fracture length. In this study, the effect of varying number of perforation clusters within a given stage ranging from 2 to 5 on the fracture propagation was investigated. Three values of stage spacing were considered including 100 ft, 200 ft, and 300 ft. After predicting the non-planar fracture geometry, the semi-analytical model is employed to simulate production from such fracture geometries. According to the well productivity, the optimal number of clusters per stage for varying stage spacing is discussed and determined. In addition, we compared the well performance with the optimal number of perforation clusters for the stage spacing of 100 ft, 200 ft, and 300 ft under a given horizontal well length of 2,500 ft. Furthermore, we used the integrated approach to analyze a well performance from Marcellus shale gas reservoirs. Through the field case study, the more-realistic non-planar fracture geometry was quantified. Also, the difference between the non-planar fracture geometry and ideal planar fracture geometry with equal fracture width and fracture half-length was compared. Finally, the optimal fracture treatment design for the field well development was recommended. This work can provide new insights into optimization of number of perforation clusters per stage within a given fracture stage and a better understanding of the non-planar fracture geometry in shale gas reservoirs.

This research aims to analyze which factors are most determining in influencing interest in using pay-later. This type of research is multivariate analytical research using a quantitative approach. All pay-later service customers who engage in e-commerce make up the study's population. Purposive sampling and non-probability sampling were employed in the sample-taking process based on this study. There are one hundred samples. Researchers have made observations to gather data. The author intends to employ principal component analysis and maximum likelihood estimation techniques for data analysis. Following the completion of the study, the following findings were made. The principal component analysis method's RMSE value is 0.02. The maximum likelihood estimates method's root mean square error is 0.015. Therefore, it can be said that the principal component analysis approach is appropriate for usage because it can explain factor formation due to its strong factor loading value and low RMSE value. The influence of advantages is the most important element determining interest in pay-later utilizing the principal component analysis method since it has the highest loading factor value in decision-making.

A novel online structure damage identification using Principal Component Analysis (PCA) techniques and the perceptron backpropagation neural network is presented. There are three phases to execute this method. In Phase I, system modal information, frequencies and mode shapes, are calculated. Phase II is for damage location identification; the Residual Force Vectors (RFVs) are computed as input to the first neural network. Then the network was trained to simulate damage location identification. Phase III is the severity identification step. The PCA method is used to modify the input for the second neural network. Then this network identifies the severity. There are three advantages of using the PCA method, First, PCA method characterizes the original modal information precisely. Second, PCA method creates the unique data for different damage cases unlike other modal property based data. Third, the accuracy of the damage identification improves significantly, when compared with previously developed methods. This method can be operated online for the real time structural damage identification.