Study on Trace Elements in Rehmannia glutinosa Libosch. by Principal Component Analysis and Clustering Analysis

[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.

Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Determination of bioactive components in the fruits of Cercis chinensis Bunge by HPLC-MS/MS and quality evaluation by principal components and hierarchical cluster analyses

The fruits of leguminous plants Cercis Chinensis Bunge are still overlooked although they have been reported to be antioxidative because of the limited information on the phytochemicals of C.chinensis fruits.A simple,rapid and sensitive HPLC-MS/MS method was developed for the identification and quantitation of the major bioactive components in C.chinensis fruits.Eighteen polyphenols were identified,which are first reported in C.chinensis fruits.Moreover,ten components were simultaneously quantified.The validated quantitative method was proved to be sensitive,reproducible and accurate.Then,it was applied to analyze batches of C.chinensis fruits from different phytomorph and areas.The principal components analysis(PCA)realized visualization and reduction of data set dimension while the hierarchical cluster analysis(HCA)indicated that the content of phenolic acids or all ten components might be used to differentiate C.chinensis fruits of different phytomorph.

Fruit and Vegetable Nutrition Value Assessment and Replacement Based on the Principal Component Analysis and Cluster Analysis

Utilizing principal component analysis (PCA) and cluster analysis, the standardization, dimension-reduction and de-correlation of multiple evaluation index system for fruit and vegetable nutrition are performed to assign principal component factor based on cluster analysis of loading matrix and combining with actual meaning and evaluation direction of index categories. To evaluate the richness of its nutrition according to the score of nutrition of fruit and vegetable, finally equivalent replacement suggestions are given in different seasons of vegetables & fruits according to the result of clustering. Studies show that principal component cluster method can not only carry on the reasonable classification of multivariate data effectively, but also make reasonable evaluation on the sample object, and provide powerful basis for evaluation of fruits and vegetables’ nutrition.

Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

Water quality monitoring has one of the highest priorities in surface water protection policy. Many variety approaches are being used to interpret and analyze the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are mainly based on statistical methods, multivariate statistical techniques in particular. In the present study, the use of multivariate techniques is required to reduce the large variables number of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs) and determination of relationships among them for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA), Fuzzy C-Means (FCM) and K-means algorithm for clustering analysis, this study attempted to determine the major dominant factors responsible for the variations of Nile River water quality upstream Cairo Drinking Water Plants (CDWPs). Furthermore, cluster analysis classified 21 sampling stations into three clusters based on similarities of water quality features. The result of PCA shows that 6 principal components contain the key variables and account for 75.82% of total variance of the study area surface water quality and the dominant water quality parameters were: Conductivity, Iron, Biological Oxygen Demand (BOD), Total Coliform (TC), Ammonia (NH3), and pH. However, the results from both of FCM clustering and K-means algorithm, based on the dominant parameters concentrations, determined 3 cluster groups and produced cluster centers (prototypes). Based on clustering classification, a noted water quality deteriorating as the cluster number increased from 1 to 3. However the cluster grouping can be used to identify the physical, chemical and biological processes creating the variations in the water quality parameters. This study revealed that multivariate analysis techniques, as the extracted water quality dominant parameters and clustered information can be used in reducing the number of sampling parameters on the Nile River in a cost effective and efficient way instead of using a large set of parameters without missing much information. These techniques can be helpful for decision makers to obtain a global view on the water quality in any surface water or other water bodies when analyzing large data sets especially without a priori knowledge about relationships between them.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Application of Principal Component Analysis, Cluster Analysis, Pollution Index and Geoaccumulation Index in Pollution Assessment with Heavy Metals from Gold Mining Operations, Tanzania

Gold mining is now widely acknowledged as one of the significant sources of soil pollution in developed countries. In developing countries, the sources and levels of soil contamination have not been thoroughly addressed. Thus, this study was intended to determine the source of soil pollution and the level of contamination in the active and closed gold mining areas. The research paper presents the pollution load of heavy metals (lead-Pb, chromium-Cr, cadmium-Cd, copper-Cu, arsenic-As, manganese-Mn, and nickel-Ni) in 90 soil samples collected from the studied sites. Multivariate statistical analysis, including Principal Component Analysis (PCA) and Cluster Analysis (CA), coupled with correlation coefficient analysis, was performed to determine the possible sources of pollution in the study areas. The results indicated that Pb, Cr, Cu and Mn come from different sources than Cd, As and Ni. The results obtained from the metal pollution assessment using the Pollution Index (PI) and the Geoaccumulation Index (Igeo) confirmed that soils in the mining areas were contaminated in the range from moderately through strongly to highly contaminated soils. This study verified that soil contamination in the gold mining areas results from natural and anthropogenic processes. The current study findings would enhance our knowledge regarding the soil contamination level in the mining areas and the source of contamination. It is recommended to use PCA, CA, PI and Igeo to assess and monitor the heavy metal contaminated soil in gold mining areas.

Comprehensive Evaluation of the Economic Development Level of Guanzhong-Tianshui Economic Zone Using Principal Component Cluster Analysis

Based on 10 years of statistics concerning economic development in Xi'an as the main part of Guanzhong- Tianshui Economic Zone, this article builds the main indicator system to reflect economic development. Using two mathematical methods( principal component analysis and cluster analysis),we carry out comprehensive evaluation analysis of the main economic indicators,point out the distribution differences in the economic development level in this region,and make classification,in order to provide a scientific basis for the decision- making body to lay down the relevant economic development strategies in accordance with the economic development level and geographical location.

Analysis of genetic diversity in banana cultivars(Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic,hierarchical clustering and principal component analyses

Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs.This study employed amplified fragment length polymorphism(AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman.Using 12 primer combinations,a total of 1094 bands were scored,of which 1012 were polymorphic.Eighty-two unique markers were identified,which revealed the distinct separation of the seven cultivars.The results obtained show that AFLP can be used to differentiate the banana cultivars.Further classification by phylogenetic,hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis.Based on the analytical results,a consensus dendrogram of the banana cultivars is presented.

Description and Classification of Leather Defects Based on Principal Component Analysis

The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a hierarchical classification for defects is proposed.Firstly,samples are collected according to the method of minimum rectangle,and defects are extracted by image processing method.According to the geometric features of representation, they are divided into dot,line and surface for rough classification. From analysing the data which extracting the defects of geometry,gray and texture,the dominating characteristics can be acquired. Each type of defect by choosing different and representative characteristics,reducing the dimension of the data,and through these characteristics of clustering to achieve convergence effectively,realize extracted accurately,and digitized the defect characteristics,eventually establish the database. The results showthat this method can achieve more than 90% accuracy and greatly improve the accuracy of classification.

Relationship of public preferences and behavior in residential outdoor spaces using analytic hierarchy process and principal component analysis—a case study of Hangzhou City, China

This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.

Multi-view recognition of fruit packing boxes based on features clustering angle

In order to realize the intelligent mechanization of the last process of the fruit industry chains,the identification of fruit packing boxes is researched.A multi-view database is established to describe the omnidirectional attitudes of the fruit packing boxes.In order to reduce the data redundancy caused by multi-view acquisition,a new binary multi-view kernel principal component analysis network(BMKPCANet) is built,and a multi-view recognition method of fruit packing boxes is proposed based on the BMKPCANet and support vector machine(SVM).The experimental results show that the recognition accuracy of proposed BMKPCANet is 12.82% higher than PCANet and3.51% higher than KPCANet on average.The time consumption of proposed BMKPCANet is 7.74%lower than PCANet and 29.01% lower than KPCANet on average.This work has laid a theoretical foundation for multi-view recognition of 3 D objects and has a good practical application value.

Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

Radio-map Establishment based on Fuzzy Clustering for WLAN Hybrid KNN/ANN Indoor Positioning

A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.

Analyzing potential tourist behavior using PCA and modified affinity propagation clustering based on Baidu index:taking Beijing city as an example

In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.

Ranking and Clustering of the Economic Status of Rural Residents in 31 Provinces and Regions in China

In order to rank and cluster the economic status of rural residents in 31 provinces, cities and autonomous regions, the MATLAB software is used and the component analysis and the cluster analysis are conducted on the data reflecting the economic status of each area. The results show that the provinces or cities with high comprehensive , scores are Shanghai Municipality, Beijing Municipality, Zhejiang Province, Jiangsu Province, Tianjin Municipality, Guangdong Province, Fujian Province, Shandong Province and Liaoning Province according to priority; the provinces or autonomous regions with low comprehensive scores are Gansu Province, Guizhou Province , Tibet, Uygur autonomous region and Yunnan Province. The economic status of rural residents in the 31 provinces and autonomous regions are partly parallel with the comprehensive economic development. The improvement of the economic status of rural residents is helpful for the overall economic elevation. Therefore, the government should coordinate the economic development of urban and rural areas, industry and agricultural, developed region and undeveloped region, and coastal areas and central and western areas to maximize the social welfare of the whole nation.

Principal Component Analysis and Cluster Analysis of Luffa Germplasm Resources in Zhejiang Province

In this study, 32 Luffa germplasm resources were collected from various regions in Zhejiang Province as experimental materials, to investigate 22 agronomic traits including fruit bearing habit, leaf margin, fruit ribbing and percentage of nodes with female flowers to total node. Based on the obtained experimental data, principal component analysis and cluster analysis were carried out using DPS software. The results showed that 22 agronomic traits could be integrated into 5 principal components, with the cumulative contributive percentage of 81. 308%. According to the correlations between the first five principal components and traits, 14 traits with great influences were screened. On the basis of principal component analysis, cluster analysis of 32 Luffa germplasm resources was conducted, which divided Luffa cylindrica and Luffa acutangula into two categories and six subcategories by Euclidean genetic distances. This study provided scientific basis for the collection, preservation, identification, creation and utilization of Luffa germplasm and parent selection in cross breeding of Luffa.

Principal component analysis and cluster analysis based orbit optimization for earth observation satellites

This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.

Clustering for Bivariate Functional Data

In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are considered.In addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component analysis.Simulation results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand index.The approaches are further illustrated through empirical analysis of human mortality data.