In this study for searching the tempo-spatial patterns and classification of monthly temperature regimes of Iran during the last half century has been analyzed using Principal Component Analysis and Cluster Analysis (CA).Analysis among the Empirical Orthogonal Functions (EOF) from principal component analysis is a powerful tool to detect temporal and spatial patterns of climatic variables. Principal component analysis may result to some empirical modes representing climatic tempo-spatial patterns. Additionally this method approach to decline the mass: of data and convert primary variables into several components which can illustrate the most effects of primary variables. A principal component analysis applied to correlation matrix of temperature data as a data reduction technique. It showed that three principal components explain about 99.7 percent of variance. The first principal component explains about 95.3 percent of variance. This component dominates southern parts of the country from Khuzestan to Chabahar shows completely different characteristic in compare with another region throughout the year. This study showed that principal component analysis is a useful tool for detecting temperature regimes in Iran.A cluster analysis on principal component scores resulted to three main temperature regions in Iran. This includes mountainous region, lowland and foothill region and south region.

One of the typical aims of statistical shape analysis، in addition to deriving an estimate of mean shape، is to get an estimate of shape variability. This aim is achived through employing the principal component analysis. Because the principal component analysis is limited to data on Euclidean space، this method cannot be applied for the shape data which are inherently non-Euclidean data. In this situation، the principal geodesic analysis or its linear approximation can be used as a generalization of the principal component analysis in non-Euclidean space. Because the main root of this method is the gradient descent algorithm، revealing some of its main defects، a new algorithm is proposed in this paper which leads to a robust estimate of mean shape and also preserves the geometrical structure of shape. Then، providing some theoretical aspects of principal geodesic analysis، its application is evaluated in a simulation study and in a real data.

Q-mode hierarchical cluster (HCA) and principal component analysis (PCA) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: June, September, and December, along the Ain Azel aquifer, Algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the aquifer scale. Hydrochemical data for 54 groundwater samples were subjected to Q-mode hierarchical cluster and principal component analysis. The study finds, from Q-mode HCA that there are three main hydrochemical facies namely the less saline water (group 1: Ca-Mg-HCO3), mixed water (group 2: Mg-Ca-HCO3-Cl) and blended water (group 3: Mg-Ca-Cl-HCO3). In principal component analysis, the first 4 factors explain 72.14% of the total variance, their loadings allowing the interpretation of hydrochemical processes that take place in the area. The results of this study clearly demonstrate the usefulness of multivariate statistical analysis in hydrochemical.

Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to identify intercorrelations among soil properties (chemical, physical and biological) using principal component analysis (PCA) and their relationships with maize yield variability in field. Site variables (18) and maize yield were measured in selected parts of Astan Quds agricultural fields in Mashhad city. The principal component analysis was used to reduce the site variables numbers and remove multicollinearity among variables. The first four PCs with eigenvalues>1 accounted for > 67% of variability in measured soil properties. Soil properties were grouped in four PCs as: (PC1) Soil highly descriptive fertility potential, (PC2) Soil moderately descriptive fertility potential, (PC3) Soil permeability potential, (PC4) Soil aggregation potential. The results showed that the factor of soil highly descriptive fertility potential explained 43% of variance and accounted for 77% of the yield variability in the field. Principal component analysis allows explaining a major part of crop yield variability by removing the multicollinearity.

This paper presents a combination of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) to reduce the dimensionality of data set. DEA is known as effective tool for assessment and benchmarking. The weak point of DEA, it is that the number of efficient DMUs relies on the number of variables (inputs and outputs). For solving this, first, we do principal component analysis (PCA) on the ratios of a single output to a single input. In order to reduce the dimensionality of data set, the required principal components have been selected from the generated ones according to the given choice principle. Then a linear monotone increasing data transformation is made to the chosen principal components to avoid being negative. Finally, the transformed principal components are treated as outputs into data envelopment analysis (DEA) models. One of the main differences of this model versus previous models is that this one's multi objective model. We used this approach to rank the bank branches of Tehran.

Summary : Principal Component Analysis (PCA) and Catchment Basin Analysis (CBA) methods have important role in investigation of stream sediment geochemical data and can have special contribution in prospecting programs and other exploration steps. In this study, the data of 843 stream sediment samples from Khoy 1:100,000 geological sheet area in West Azerbaijan, were investigated using PCA and CBA methods. At first, dimension of variables such as REEs as well as Cu, Pb, Zn, Cr, Au and Ag was decreased using principal component analysis (PCA) and the main principal components were selected for further investigation and finally the correlation of each parameter with achieved factors were given. Then, the background values were calculated and local anomalies were separated using catchment basin analysis on the basis of real area and upstream geological units of catchment. In addition, it is demonstrated that catchment basin analysis is able to better discriminate local anomalies. Consequently two mentioned methods obviously can validate each other.Briefly, in this research, the rare earth and other strategic elements in stream sediments samples of the Khoy 1:100,000 geological sheet area, have been investigated. The background values were calculated and local anomalies were separated using catchment basin analysis on the basis of real area and upstream geological units of catchment. Threshold level method was used for separation of anomalies from background. Also, by principal component analysis (PCA) method, the main factors were delineated. Results obtained from PCA method, were estimated using ordinary kriging and finally these two methods were compared. Consequently two mentioned methods obviously can validate each other.

Aggregation of elements in stream sediments depends on the stream network, area and topography slope of catchment and upstream lithological units. Investigation of these sediments using catchment basin analysis and principal component analysis methods, obviously introduces elements dispersion and prevents demonstrating unreal anomalies. The PCA method is useful in identification of complicated structure, indexing and reduction of data dimension. Because of existence of vast alluvial plains, alluvial fans, coasts and vast basins in Iran, geochemical data analysis by CBA method can lead us to explore the valuable mineral resources to reach sustainable development. The above mention methods will cause to find probable mineralization regions.
Methodology and Approaches : In this study, for investigation of stream sediments geochemical anomalies, different steps such as data processing, separation of basins, calculation of local anomaly using CBA and PCA methods have been used for REEs and some other trace elements. In CBA method, the basins were separated by ArcGIS software and Archydro tools extension and then, real area of geological units for all catchments were calculated. Finally, using real area and application of upstream lithologies in given equations, the background values were calculated and local anomalies were separated. In PCA method, the main component of each elements were delineated, using ordinary kriging, values of each main factors have been estimated in unsampled regions.
Results and Results and geochemical maps obtained by BCA and PCA methods demonstrate that catchment basin analysis is able to better discriminate local anomalies and these anomalies have been correlated with the ordinary kriging maps obtained from principal component analysis and these two mentioned methods, obviously can validate each other.

Rabor area is located in 160 km south of Kerman city and 40 km east of Baft. There is some evidence illustrating some porphyry copper type mineralization, co-operated with tens of within Urumieh-Dokhtar volcanic belt stocks. Identification of the high potential localities and mapping the porphyry copper mineralization within these sites look very necessary. To aim for this goal, we aimed to identify the probable mineralization zones related porphyry copper mineralization alteration haloes in Rabor. In this research, by using the satellite image processing of ASTER sensor, applying the methods such as band ratioing, principal component analysis (PCA) and selective principal component analysis (Crosta) as well as the direct data from the Baft geological map (1:100000), available metallogenical theories and porphyry copper mineralization models, prepare images based on available clay mineral concentration maps from the region could provide evidences for an existence of a porphyry copper mineralization. Band ratioing was applied to discriminate the altered areas from the non-altered ones and also area lithology, porphyry copper deposit boundaries by identification of kaolinite, alunite and illite as indicator minerals within the studied area. Selective principal component analysis was also applied to produce the clay mineral concentration indicator maps to potential mining area recognition. Ore index cross matching called Pey Negin based recognition presumed area, demonstrates the selective principal component analysis method accuracy and its efficiency by using the satellite ASTER data from the altered area.

Nowadays, socioeconomic status (SES) is an important predictor of morbidity and mortality and also is very important for disease prevention and intervention efforts in health policy activities. In this article, while reviewing the history, different levels and techniques of measuring socioeconomic status, the application of "Principal component analysis" in studies of the relationship between socioeconomic status and health status was investigated.
To search for articles in this review study, the databases PubMed, Science Direct, Google scholar and inside the country databases used. Search by key words: Principal component analysis, Socioeconomic status, Health status was conducted. Finally, the 36 references between the years of 2000 and 2014 and four relevant and valid references before 2000 which were more consistent with the subject and objectives of the study selected for report and presented in form of a review article.
Review of existing evidence indicates that SES is a very complex and multidimensional issue and there is no gold standard method for its measuring and because some of this measurement such as income, occupation, consumption or expenditure is not simply possible or reliable, therefore researchers use other proxies as alternate representative. To accumulate a large number of variables in one or more proxies, different methods can be used. The most common of them is principal component analysis (PCA) that is a multivariate statistical technique for reducing the number of variables in a data set without losing too much information.
Socioeconomic status is associated with a broad range of health outcomes and Principal component analysis is a good way for weighting the SES variables in studies of SES and Health.

The effect of precipitation changes on water resources, agricultural production reveals the need for accurate methods for precipitation forecasting. In this research, one of the soft computing methods was developed in order to forecast precipitation with the data reduction approach. Input data of model was mean air temperature, dew point temperature, mean sea level pressure, mean station pressure, mean relative humidity and mean wind speed at Tabriz, Ahar and Jolfa stations. The method used in this study includes Epsilon and Nu support vector regression. In all studied stations, the use of Nu support vector regression compared to Epsilon reduced the error so that UII values ​​with Nu support vector regression in Tabriz, Ahar and Jolfa stations were decreased 19.19, 5.88 and 15.78%, respectively. The results indicate the limitation of using the data reduction approach for data with KMO factor lower than 0.5, which included Tabriz and Ahar stations. Principal component analysis in both types of support vector regression increased the performance of the model so that in Jolfa station by using principal component analysis d values ​​of Epsilon and Nu support vector regression increased by 16.6 and 17.5%. Execution of Verimax rotation in preprocessing of input data to regression was stronger than principal component analysis. In this regard, RRMSE and RMSE values ​​in Jolfa station using Epsilon support vector regression were decreased 6.66 and 6.45%. Therefore, principal component analysis is a suitable tool to improve the performance of soft computing methods by regarding the relevant constraints.

The main purpose of this paper is to apply statistical data analysis to find an acceptable geochemical pattern for Qanat Marvan area. Geological and geochemical information of Pb-Zn mineralization of Qanat Marvan district is first introduced and then data input to the Surfer software, contour map, drawing and principal component analyses are carried out. Pb-Zn thresholds are determined with fractal method and subsequently the sericitization index and its contour map are prepared. Finally, with respect to correlation coefficient matrices, principal component analyses, hierarchical clustering and the produced maps, it can be concluded that Pb-Zn elements in the PC2 and PC3 have high values. Therefore, principal component score map PC4 is equal to the Pb-Zn contour maps, suggesting component mineralization of Pb-Zn in the study area. These areas have undergone phyllic (sericitic) alteration due to Pb-Zn mineralization.