Universal Database Cluster Solution -- Based on Goldengate

2011 ◽  
Author(s):  
Yong Xiao ◽  
Qianrang Gong
Keyword(s):  
Cluster Solution ◽  
Database Cluster

scholarly journals Heart and Mind: Essential Components in Sound Decision-Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 364-364
Author(s):  
Michaela Clark ◽  
Julie Hicks Patrick ◽  
Michaela Reardon
Keyword(s):  
Decision Making ◽  
Decision Quality ◽  
Cluster Solution ◽  
Cognitive Resources ◽  
Cluster Membership ◽  
Essential Components ◽  
Decision Making Processes ◽  
Affective Components ◽  
Heart And Mind

Abstract Consumer tasks permit an ecologically-valid context in which to examine the contributions of affective and cognitive resources to decision-making processes and outcomes. Although previous work shows that cognitive factors are important when individuals make decisions (Patrick et al., 2013; Queen et al.), the role of affective components is less clear. We examine these issues in two studies. Study 1 used data from 1000+ adults to inform a cluster analysis examining affective aspects (importance, meaningfulness) of making different types of decisions. A 4-cluster solution resulted. In Study 2, we used affective cluster membership and cognitive performance as predictors of experimental decision-making outcomes among a subset of participants (N = 60). Results of the regression (F(2, 40) = 6.51, p < .01, R2 = .25.) revealed that both the affective clusters (b = .37, p = .01) and cognitive ability (b = -.30, p = .04) uniquely contributed to the variance explained in decision quality. Age did not uniquely contribute. Results are discussed in the context of developing measures that enable us to move the field forward.

scholarly journals Evaluation of a High-Resolution SPI for Monitoring Local Drought Severity

2016 ◽  
Vol 55 (10) ◽  
pp. 2247-2262 ◽  
Author(s):  
Rebecca V. Cumbie-Ward ◽  
Ryan P. Boyles
Keyword(s):  
United States ◽  
High Resolution ◽  
Standardized Precipitation Index ◽  
Stage Iv ◽  
Drought Severity ◽  
Cluster Solution ◽  
Historical Distribution ◽  
Drought Conditions ◽  
Hydrologic Prediction ◽  
Strong Agreement

AbstractA standardized precipitation index (SPI) that uses high-resolution, daily estimates of precipitation from the National Weather Service over the contiguous United States has been developed and is referred to as HRD SPI. There are two different historical distributions computed in the HRD SPI dataset, each with a different combination of normals period (1971–2000 or 1981–2010) and clustering solution of gauge stations. For each historical distribution, the SPI is computed using the NCEP Stage IV and Advanced Hydrologic Prediction Service (AHPS) gridded precipitation datasets for a total of four different HRD SPI products. HRD SPIs are found to correlate strongly with independently produced SPIs over the 10-yr period from 2005 to 2015. The drought-monitoring utility of the HRD SPIs is assessed with case studies of drought in the central and southern United States during 2012 and over the Carolinas during 2007–08. A monthly comparison between HRD SPIs and independently produced SPIs reveals generally strong agreement during both events but weak agreement in areas where radar coverage is poor. For both study regions, HRD SPI is compared with the U.S. Drought Monitor (USDM) to assess the best combination of precipitation input, normals period, and station clustering solution. SPI generated with AHPS precipitation and the 1981–2010 PRISM normals and associated cluster solution is found to best capture the spatial extent and severity of drought conditions indicated by the USDM. This SPI is also able to resolve local variations in drought conditions that are not shown by either the USDM or comparison SPI datasets.

A fluxional tetraplatinum cluster: solution and solid state structure of the cation [Pt4(µ-H)(µ-CO)2(µ-Ph2PCH2PPh2)3(Ph2PCH2PPh2)]+

1988 ◽  
Vol 0 (2) ◽  
pp. 149-150 ◽  
Author(s):  
Graeme Douglas ◽  
Ljubica Manojlović-Muir ◽  
Kenneth W. Muir ◽  
Michael C. Jennings ◽  
Brian R. Lloyd ◽  
...  
Keyword(s):  
Solid State ◽  
Cluster Solution ◽  
State Structure ◽  
Solid State Structure

scholarly journals An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences

Birds ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 250-260
Author(s):  
Christoph Randler
Keyword(s):  
Cluster Analysis ◽  
Optimal Solution ◽  
Social Bonding ◽  
Cluster Solution ◽  
Novice Group ◽  
Wide Range ◽  
German Speaking ◽  
Group Members ◽  
Bird Group ◽  
And Gender

The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.

scholarly journals Different patterns of behavioural and emotional difficulties through adolescence: The influence of prosocial skills

2016 ◽  
Vol 33 (1) ◽  
pp. 48 ◽  
Author(s):  
Javier Ortuño-Sierra ◽  
Eduardo Fonseca-Pedrero ◽  
Sylvia Sastre i Riba ◽  
José Muñiz
Keyword(s):  
Age Groups ◽  
Cluster Solution ◽  
Emotional Difficulties ◽  
Prosocial Skills ◽  
School Based ◽  
Gender And Age ◽  
Behavioural Patterns ◽  
Cluster Composition ◽  
Strengths And Difficulties ◽  
Internalizing Difficulties

<p>The main purpose of the study was to examine the cluster composition of the analysis on the effect of gender and age of the Strengths and Difficulties Questionnaire (SDQ) in a large school-based sample of high school adolescents ranging from 14 to 18 years old (<em>N</em> =  1474). In order to do this, a K-means iterative cluster analysis was performed. A five-cluster solution turned out to be the most parsimonious in the differentiation of emotional and behavioural patterns. A five-cluster solution yielded the following patterns: “No difficulties and high prosocial scores” (<em>n </em>= 418; 28.36%), “high difficulties and low prosocial scores” (<em>n</em> = 239; 16.21%), “high on hyperactivity, low on the rest of the difficulties subscales, and high in prosocial capabilities” (<em>n</em> = 302; 20.49%), “high on emotional and peer problems, relatively low on conduct and hyperactivity, and high in prosocial capabilities” (<em>n</em> = 275; 18.66%), and finally “hyperactivity problems and average in the others difficulties subscales, and in prosocial capabilities” (<em>n</em> = 239; 16.21%). This cluster solution was replicated attending to the same gender and age groups. Nevertheless, differences in the distribution of the cluster composition suggest that difficulties differ by gender and age. The results allow for the conclusion that men reveal a greater number of problems of an externalizing nature whereas women indicate a greater degree of problems with internalizing difficulties and prosocial skills.</p>

scholarly journals Motivational profiles and their relationships with basic psychological needs, academic performance, study strategies, self-esteem, and vitality in dental students in Chile

2018 ◽  
Vol 15 ◽  
pp. 11 ◽  
Author(s):  
Cesar A. Orsini ◽  
Vivian I. Binnie ◽  
Jorge A. Tricio
Keyword(s):  
Academic Performance ◽  
Intrinsic Motivation ◽  
Self Esteem ◽  
Psychological Needs ◽  
Study Strategies ◽  
Dental Students ◽  
Basic Psychological Needs ◽  
Cluster Solution ◽  
Controlled Motivation ◽  
Motivational Profiles

Purpose: To determine dental students’ motivational profiles through a person-centred approach and to analyse the associations with the satisfaction of their basic psychological needs, study strategies, academic performance, self-esteem, and vitality. Methods: A total of 924 students from the University of San Sebastian (Chile) participated in this cross-sectional cor¬relational study in spring 2016. Data were collected through 5 self-reported instruments, in addition to students’ academic performance. The Cronbach alpha, descriptive statistics, and correla¬tion scores were computed. A k-means cluster analysis with intrinsic and controlled motivation was conducted to identify different mo-tivational profiles. Subsequently, multivariate analysis of covariance controlling for the effects of gender and year of study was carried out to assess differences among the retained motivational profiles and learning variables. Results: All instruments showed acceptable Cronbach alpha scores. A 4-cluster solution was retained for the motivational profile over a 3- or 5-cluster solution. Students’ motiva-tional profiles were characterized by different degrees of intrinsic and controlled motivation. The high intrinsic motivation groups showed higher perceptions of their basic psychological, a greater propensity for a deep rather than surface study strategy, better academic performance, and higher scores for self-esteem and vitality than the low intrinsic motivation groups, regardless of the degree of controlled motivation. Conclusion: Students with a high intrinsic motivation profile, regardless of their controlled motivation scores, reported better learning characteristics. Therefore, special attention should be paid to students’ motivational profiles, as the quality of motivation might serve as a basis for interventions to support their academic success and well-being.

A Head-to-Head Comparison of Big-Five Types and Traits in the Prediction of Social Attitudes

2007 ◽  
Vol 28 (3) ◽  
pp. 138-149 ◽  
Author(s):  
Marcus Roth ◽  
Gernot von Collani
Keyword(s):  
Big Five ◽  
Belief Systems ◽  
Social Dominance Orientation ◽  
Social Attitudes ◽  
Cluster Solution ◽  
Big Five Personality ◽  
Personality Variables ◽  
Right Wing Authoritarianism ◽  
Show Evidence ◽  
Typological Approach

Abstract. Based on the recent findings that show evidence in favor of five rather than three prototypes in Big Five questionnaire data, the main aim of this study was to evaluate a five-cluster solution. The cluster solution is compared with a Big Five variable-centered approach regarding their relative utility in the concurrent prediction of social attitudes in an adult, nonstudent sample. In addition to the Big Five personality variables, we also assessed social attitudes (generalized prejudice, right-wing authoritarianism, social dominance orientation) and resiliency. Performing head-to-head comparisons to evaluate the goodness of cluster-solutions, our study again provides strong evidence that the five-cluster solution is superior to the three-cluster solution. The results also suggest that in fair comparisons (e.g., dichotomized Big Five scores) the typological approach can compete with the dimensional approach in predicting criterion variables. Furthermore, we have been able to show that the typological approach based on Big Five personality variables can lead to reasonable predictions and new insights with respect to social attitudes and ideological belief systems.

A taxonomy of the scope and organization of local sexually transmitted disease services for policy and practice

2018 ◽  
Vol 29 (14) ◽  
pp. 1375-1383
Author(s):  
Hector P Rodriguez ◽  
Summer Starling ◽  
Zosha Kandel ◽  
Robert Weech-Maldonado ◽  
Nicholas J Moss ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Critical Role ◽  
Transmitted Disease ◽  
The United States ◽  
Incidence Rates ◽  
Contact Tracing ◽  
Cluster Solution ◽  
Local Health ◽  
Sexually Transmitted

Local health departments (LHDs) and their organizational partners play a critical role in controlling sexually transmitted diseases (STDs) in the United States. We examine variation in the differentiation, integration, and concentration (DIC) of STD services and develop a taxonomy describing the scope and organization of local STD services. LHD STD programs (n = 115) in Alabama (AL) and California (CA) responded to surveys assessing STD services available in 2014. K-means cluster analysis identified LHD groupings based on DIC variation. Discriminant analysis validated cluster solutions. Differences in organizational partnerships and scope of STD services were compared by taxonomy category. Multivariable regression models estimated the association of the STD services organization taxonomy and five-year (2010–2014) gonorrhea incidence rates, controlling for county-level sociodemographics and resources. A three-cluster solution was identified: (1) low DIC (n = 74), (2) moderate DIC (n = 31), and (3) high DIC (n = 10). In discriminant analysis, 95% of jurisdictions were classified into the same types as originally assigned through K-means cluster analysis. High DIC jurisdictions were more likely (p < 0.001) to partner with most organizations than moderate and low DIC jurisdictions, and more likely (p < 0.001) to conduct STD needs assessment, comprehensive sex education, and targeted screening. In contrast, contact tracing, case management, and investigations were conducted similarly across jurisdictions. In adjusted analyses, there were no differences in gonorrhea incidence rates by category. Jurisdictions in CA and AL can be characterized into three distinct clusters based on the DIC of STD services. Taxonomic analyses may aid in improving the reach and effectiveness of STD services.

scholarly journals Using Clustering as a Tool: Mixed Methods in Qualitative Data Analysis

2015 ◽  
Author(s):  
Laura Macia
Keyword(s):  
Cluster Analysis ◽  
Mixed Methods ◽  
Data Analysis ◽  
Qualitative Data ◽  
Similarity Measures ◽  
Cluster Solution ◽  
Clustering Methods ◽  
Qualitative Data Analysis ◽  
Research Project ◽  
Detailed Explanation

In this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be particularly helpful in identifying patterns where numerous cases are studied. I use as illustration a research project on Latino grievances to offer a detailed explanation of the main steps in cluster analysis, providing specific considerations for its use with qualitative data. I specifically describe the issues of data transformation, the choice of clustering methods and similarity measures, the identification of a cluster solution, and the interpretation of the data in a qualitative context.

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