This study uses cluster analysis as the methodology to explore policy of the asset allocation as well as the selection of equities under the multiple-factor asset pricing models. Based on the data of financial market recorded on Bloomberg from 2000/1/4 to 2012/2/10, the conclusions of this study are summarized as following: First at all, under the significance level of 5%, P/S ratio should be included in the multiple-factor asset pricing model. Nonetheless, the significance of proxy agent of foreign exchange volatility in terms of 11-day moving average of USD/JPY foreign exchange spot rate, as well as the interest spread in terms of yields on 10-year US government bond subtracting 3-month US treasury bill cannot pass the required significance level. Second, the rates of stock return as Qualcomm, Intel and Texas instruments in the industry supply chain of technology products, will be positively related to interest spread, with the variable of ¡§Foreign_Volitility¡¨ negatively related to those rates of return as well as sales growth momentum positively related to those. As far as those rates of stock return 3C brand companies such as Apple, Microsoft, Dell and IBM, the interpreting capability of variable of ¡§Foreign_Volitility¡¨ under the assumptions of market structure in this study, will be mixed, with the interest spread positively related to those returns and P/S ratio generating mixed outcomes. As far as those equities such as GE, Procter & Gamble, Home Depot, Tiffany, AIG, NIKE, Exxon Mobile Corp, the interpreting capability of variable of ¡§Foreign_Volitility¡¨ under the assumptions of market structure in this study, will be negatively related to stock return except for Exxon Mobile Corp, with the interest spread generating mixed outcomes and P/S ratio positively related to those returns.

It is not easy to state clearly to the public for quality of groundwater bodies, even if there are a large number of effective water quality data, it is still hard to combine and induct,and it often occurs in different units have each put forward to explain on the test results.Multivariate statistical analysis method can simplify high complex data into a representative function of the small number of factors, clearly explained to a group of inter-relationship of the original variables, or to be clustered and identified according to the similarity between the data to understand the reason behind the formation of certain phenomena, so this study utilize it to explore the groundwater characteristics. In this study, monitoring data come from the Kaohsiung city 48 groundwater monitoring wells of the EPA National Water Quality Monitoring Information website database, apply SPSS12.0 package software to execute multivariate statistical analysis, including factor analysis ,cluster analysis and discriminant analysis, and thus induction, sorting and classification of water quality characteristics, evaluating the causes of pollution and local area characteristics. The results of factor analysis to obtain the groundwater quality of the Kaohsiung region 4 representative factors: the factor of salinization, organic pollution factor, the factor of ore melting and acid-base factor. Four principal component factors instead of the 17 analysis projects of the regional groundwater quality in Kaohsiung city, the variance amounted to 78.3%. Use of cluster analysis of the 48 monitoring wells in the region is divided into four groups, according to the different nature of the monitoring data and the nature of similarity and group, to investigate the correlation between the monitoring well water quality within each cluster and the main factor, and by monitoring wells position to distinguish between the average underground water quality of inland area than the coastal area, we can get the results of seawater intrusion and salinization phenomena in coastal area, and monitoring wells located in the Cijin district are polluted by the pH factor. Kaohsiung regional groundwater quality is generally in the case of hard water to very hard water. In order to understand the difference of the multivariate statistical analysis method and the general groundwater pollution index analysis, draw Piper water quality diamond cluster analysis diagram to compare the similarities and differences,the results show that the multivariate statistical analysis can supply a systematic analysis of variable data and the overall variations of the water quality, and objective clustering, while the general composite index analytcial method such as Piper, by the characteristic position to get the type of pollution, but difficult to explain the overall pollution characteristics. At last, in this study, the hope to recommend the pollution control assessment and prevention strategies of Kaohsiung city underground water.

Financial time-series forecasting has long been discussed because of its vitality for making informed investment decisions. This kind of problem, however, is intrinsically challenging due to the data dynamics in nature. Most of the research works in the past focus on artificial neural network (ANN)-based approaches. It has been pointed out that such approaches suffer from explanatory power and generalized prediction ability though. The objective of this research is thus to propose a hybrid approach for stock price forecasting. Independent component analysis (ICA) is employed to reveal the latent structure of the observed time-series and remove noise and redundancy in the structure. It further assists clustering analysis. Support vector regression (SVR) models are then applied to enhance the generalization ability with separate models built based on the time-series data of companies in each individual cluster. Two experiments are conducted accordingly. The results show that SVR has robust accuracy performance. More importantly, SVR models with ICA-based clustered data perform better than the single SVR model with all data involved. Our proposed approach does enhance the generalization ability of the forecasting models, which justifies the feasibility of its applications.

One hundred fifty-five peach (Prunus persica) cultivars, from Asia, Brazil, Europe, and the USA, were examined using eleven Simple Sequence Repeats (SSRs) to study the genetic relationships among low chill as compared to high chill peach germplasm. Data was analyzed by NTSYSpc to form a similarity matrix using Nei and Li’s Dice similarity coefficient. This similarity matrix was then subjected to a cluster analysis and a dendrogram was constructed using the UPGMA (Unweighted Pair-Group Method, Arithmetic Mean) method. A wide range of diversity was detected, from 0.33 coefficient of similarity amongst the Thai peaches to 0.97 between two Brazilian peaches. The most distant clusters were the low chill peaches from Thailand and Taiwan and the local cultivars (both fruit and ornamental types) from China. Among the improved germplasm, there were distinct clusters for the Chinese/Japanese cultivars, three clusters for the Brazilian cultivars and one for the cultivars from the USA and Europe. The Brazilian materials clustered according to breeding programs in São Paulo and Pelotas reflecting the different sets of local cultivars used in the breeding efforts. The largest group investigated was the European/USA peaches. This group subdivided into three distinct clusters, with a general clustering of the low chill germplasm. The low chill accessions from Asia were genetically distant from the improved low chill peaches from the USA or Brazil. The low chill peaches from the Americas were more closely related to the high chill peaches developed in the USA and China/Japan due to the introgression of this germplasm into a low chill background.

Abstract Political behavior in organization is undeniable, while studies of organizational politics lasting for recent decades had accumulated abundant understanding in this area. However, there are still many arguments against this complex subject needed further research. The purpose of the research is 1) to test and verify the input model of Organizational Politics Perception Model¡C 2) to test the effectiveness and reliability and solve the controversy of dimensions of POPS ¡C 3) to further refine the input model by type analysis and try to explain the cause of Organizational Politics Perceptions¡C For research procedure and sample: Participants were 973 and drawn from Hospital¡Bprivate and public service firms¡Btraditional manufacturing and high-technical firms¡CThe research method is descriptive statistics analysis¡B reliability analysis¡Bfactor analysis¡Bregression analysis¡Bone-way ANOVA and cluster analysis¡C

This work investigated the characteristics of polychlorinated dibenzo-p-dioxins anddibenzofurans (PCDD/F) in stack-flue gases from six stationary emission sources in five types ofincinerators: industrial waste incinerator (IWI), small-scale municipal solid waste incinerator(MSWI), medical waste incinerator (MWI), cement kilns (CK), and crematories (CR). These characteristics were further investigated using factor analysis and cluster analysis. Experimental results reveal that PCDDs dominate MSWI and CR, and PCDFs dominate IWIa, IWIb, CK and MWIs. The factor analysis results showed that CR and MSWI have similar fingerprints, as do IWIb and MWI3. The cluster analysis showed that if a vertical line is cut at a rescaled distance of four, then the PCDD/F congener profiles fall into four groups. The indicators of PCDD/Fs are OCDD, 1,2,3,4,6,7,8-HpCDF, 2,3,4,7,8-PeCDF, and 1,2,3,4,6,7,8-HpCDD. The emission factors of PCDD/Fs herein were from 0.0433

In this thesis, the technologies that appeared to be strategic according to the Vision 2023 Technology Foresight Project were examined in terms of how they might form up technology clusters. This thesis aims to identify technology clusters in terms of common knowledge base and to use these clusters in future scenarios as a foresight tool. In this study, Vision 2023 Delphi survey respondents&rsquo / intersecting expertise levels in different fields were accepted as indicators of common knowledge base in these fields and technology clusters were formed up in this direction. In order to attain technology clusters, the appropriateness of hierarchical and nonhierarchical clustering methods and projection techniques were examined. Taking the clusters into consideration, Ward&rsquo / s method revealed the healthiest results for our data set. Investigation of scenario building which had not been used in Turkey as a an effective foresight tool, forms the second step of this study. Scenario method was examined from a historical perspective and different approaches were investigated. Finally, using the technology clusters that were gained through Ward clustering, a scenario building study by scenario matrix was conducted as an example.

Turkish Electricity Market is modeled as a non-cooperative game with complete information in order to simulate the behavior of market participants and analyze their possible strategies. Player strategies are represented with multipliers in a discrete strategy set. Different market scenarios are tested through different game settings. As the novelty of this thesis, similar market participants are clustered and treated as single players in order to apply game theory in an efficient way. Generators are clustered using Agglomerative Hierarchical Clustering and Square Sum of Deviations is used as the proximity measure. The game is constructed with three players that reflect the main characteristics of the market participants. Clusters and game scenarios are constructed using the real market data of the Turkish Electricity Market at four different time points in 2008 and results are compared. Clustering results reflect the actual installed capacity distribution based on the main companies and fuel types in Turkish Electricity Market. According to four games of clustered electricity generators, when there is not enough competition in the market, dominant player is advised to submit bids with lower price for energy surplus cases and offers with higher price for energy deficit cases. However, when there is competition in the market, players are advised to submit offers with lower price in order to take a share of the limited demand for up-regulation.

In this study, current infrageneric taxonomic status of Limonium Mill. in Turkey was assessed by using numerical taxonomy (phenetics) method. Herbarium materials belong to 21 species of Limonium Mill., collected all around Turkey, and were examined. In order to evaluate the infrageneric status, 52 characters based on ecological and morphological features of the genus were determined and recorded to construct a data matrix which was analyzed by MultiVariate Statistical Package (MVSP) and Statistica software with Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method to construct phenograms. Then, by assesing the phenograms, an overall comparison of Limonium Mill. species were made and infrageneric taxonomic status of the genus was discussed. The result of this study indicated that 5 sections of the genus are represented in phenograms which was a compatible result with theoretical information written in 7th Volume of &ldquo / Flora of Turkey&rdquo / . Moreover, in UPGMA trees, a new section (belonging Section Limonium) is observed. Section nova species live only near seashore and distinctively separated from other Section Limonium species in phenograms and Principle Component Analysis (PCA). PCA was carried out to indicate most important characters used in this study. In addition, in this study, a dichotomous key comprising all the species of Limonium Mill. in Turkey was also presented. In appendix part, brief information about each species such as distribution, habitat, conservation status, habit, endemism ratio, flowering period, IUCN categories was given to review all Limonium Mill. species in Turkey.

The normal procedure for the detection of underwater stationary targets is mainly by the application of side-scan sonar. In addition, the identification of targets within the side-scan sonar imagery is primarily based on the visual observation of the operator. Due to its complexity and poor effectiveness, the visual observation procedure was gradually been substituted by numerical analysis procedures and programs. The purpose of the current investigation was dedicated to the development of an automatic image analysis program for the detection and identification of cubic concrete artificial reefs (2 m x 2 m x 2m) in the south-western coastal area off Taiwan. The major components and methodologies of the program include: (1)Image acquisition; side-scan sonar at 500 kHz and slant range at 75 m. (2)Feature extraction; grey level co-occurrence matrix. (3)Feature Classification; unsupervised Bayesian classifier. (4)Target identification; cluster analysis. (5)Target properties analysis, includes circumference, area, central coordinates and quantity of the targets. Program verification and optimal parameters determination were conducted with a sonograph (650 ¡Ñ 650 pixels) acquired at the Chey-Ding artificial reef site off Kaohsiung County. Feature functions employed in this program include entropy, homogeneity, and mean value. The identification accuracy can reach 93% at the most. In addition, the number of artificial reefs estimated by the program was within 9 to 20, while the actual number is 15. A realistic evaluation of this program was conducted with a sonograph (2048 ¡Ñ 6050 pixels) acquired at Fang-Liau artificial reef site off Pyngdong County. In addition to the cubic reefs, the targets at this site include cross-shaped artificial reefs with dimensions less than the cubic reefs. The sonograph was divided into smaller blocks with dimensions of 2048 x 550 pixels during evaluation. The results showed that each block can be evaluated based on the value of the seed point obtained by cluster analysis. The seed point which fells between 20.6 and 24.4 indicates that there are cubic reefs existed. Between 15.3 and 17.4 indicates that there are targets with smaller dimensions (i.e., crossed reefs) existed which can not be identified properly. Between 10.1 and 10.9, there is no target existed on the seafloor. The results indicated that the number of targets identified is between 122 and 240. According to the results of this investigation, the automatic image analysis program can improve the detection and identification of stationary targets within side-scan sonar imagery.