scholarly journals Investigating Selection Criteria of Constrained Cluster Analysis: Applications in Forestry

2020 ◽  
pp. 161-180
Author(s):  
Gavin Richard Corral
Keyword(s):  
Cluster Analysis ◽  
Selection Criteria

scholarly journals Study of multivariate analysis of quantitative traits in Iranian pumpkin lines

Genetika ◽  
2017 ◽  
Vol 49 (2) ◽  
pp. 425-433
Author(s):  
Mehrab Yadegari ◽  
Mohammad Ansari
Keyword(s):  
Cluster Analysis ◽  
Seed Yield ◽  
Selection Criteria ◽  
Quantitative Traits ◽  
Indirect Selection ◽  
Fruit Weight ◽  
Diameter Ratio ◽  
Fruit Length ◽  
Yield Production ◽  
Fruit Diameter

In this study, seed yield production and its different components fruit length, fruit diameter, fruit length/fruit diameter ratio (FL/FD), diameter of flesh, diameter of seed core, fruit weight, weight of 1000 seed from 24 lines of pumpkin grown in Iran was examined. Twenty-five characters in all plant lines were measured by Descriptor (UPOV) and data were subjected to cluster analysis. Results showed that plants lines were divided in four groups. In all groups, regression comparisons were made for modeling the effect of different characters on seed yield, results also showed that fruit weight and fruit length in all groups had the most direct effect on seed yield. In conclusion, these traits are suggested as the best indirect selection criteria to improve the seed yield genetically in Cucurbita spp. genotypes especially in preliminary generation of breeding and selection programs.

The Distance and Cluster Procedure

2010 ◽  
pp. 194-224
Author(s):  
Sean Eom
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Selection Criteria ◽  
Distance Matrix ◽  
Reduction Technique ◽  
Cluster Procedure ◽  
The Creation ◽  
Data Reduction Technique

This chapter describes the distance and cluster procedure of the SAS system. SAS version 9 introduced the proc distance procedure. All previous versions of SAS used two programs (xmacro.sas and distnew.sas) to process a transposed cocitation matrix (input) to produce a distance matrix (output). Cluster analysis is a data reduction technique for grouping various entities (individuals, variables, objects) into clusters so that the entities in the same cluster have more similarity to each other with respect to some predetermined selection criteria. The first section of this chapter explains the creation of a distance matrix, which is the input to the cluster procedure. The second part of this chapter focuses on the PROC CLUSTER statement which sets out the CLUSTER procedure steps. This chapter also includes the discussions of interpreting results of cluster analysis.

Segmenting logistics service users based on their provider selection criteria to succeed in outsourcing

2020 ◽  
Vol 48 (9) ◽  
pp. 963-984
Author(s):  
Gerald Oeser
Keyword(s):  
Cluster Analysis ◽  
Analysis Of Variance ◽  
Selection Criteria ◽  
Service Providers ◽  
Core Competencies ◽  
Performance Ratio ◽  
Service Users ◽  
Logistics Outsourcing ◽  
Content Type ◽  
Logistics Service

PurposeLogistics service providers (LSPs) may invest a lot of time in tenders unsuccessfully, as they do not meet the expectations of logistics service users (LSUs). In order to help them classify and target their customers more efficiently and effectively and make logistics outsourcing more successful for both LSUs and LSPs, this paper analyzes underlying dimensions of criteria German manufacturing and trading companies actually use in selecting an LSP and clusters of LSUs based on these dimensions.Design/methodology/approachA questionnaire survey with 110 manufacturing and 135 trading companies was conducted in Germany. Principal component analysis (PCA), cluster analysis, multivariate analysis of variance, analysis of variance and discriminant analysis were performed on the sample data.FindingsPCA revealed eight dimensions of LSU criteria in selecting LSPs and that cost alone seems not decisive. Based on these dimensions, cluster analysis produced nine LSU groups. These groups differ the most in the selection criteria dimensions cost-performance ratio, operational collaboration, quality and locations. Recommendations for servicing these groups are given. The two largest groups, which make up 43.5%, seem not that demanding and price sensitive. The selection criteria dimensions and LSU groups enable LSPs to classify and target their customers more efficiently and effectively, to evaluate and develop their core competencies, and contribute to successful logistics-outsourcing relationships.Originality/valueThis research is the first to examine selection criteria dimensions and resulting clusters of German manufacturing and trading companies in order to make logistics outsourcing more successful.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Current Trends in the Training and Utilization of Paraprofessionals in Speech and Language Programs

1991 ◽  
Vol 22 (2) ◽  
pp. 51-59 ◽  
Author(s):  
Kathy L. Coufal ◽  
Allen L. Steckelberg ◽  
Stanley F. Vasa
Keyword(s):  
Selection Criteria ◽  
Job Descriptions ◽  
Paraprofessional Training ◽  
Language Programs ◽  
Program Administrators ◽  
Current Trends ◽  
Training And Supervision ◽  
Communicative Disorders ◽  
Supervision Practices ◽  
A Current

Administrators of programs for children with communicative disorders in 11 midwestern states were surveyed to assess trends in the training and utilization of paraprofessionals. Topics included: (a) current trends in employment, (b) paraprofessional training, (c) use of ASHA and state guidelines, and (d) district policies for supervision. Selection criteria, use of job descriptions, training programs, and supervision practices and policies were examined. Results indicate that paraprofessionals are used but that standards for training and supervision are not consistently applied across all programs. Program administrators report minimal training for supervising professionals.

Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

2020 ◽  
pp. 1-25
Author(s):  
Matthew L. Hall ◽  
Stephanie De Anda
Keyword(s):  
Cluster Analysis ◽  
Exposure Assessment ◽  
Hard Of Hearing ◽  
Assessment Tool ◽  
Communication Mode ◽  
Developmental Potential ◽  
Language Access ◽  
Retest Reliability ◽  
Language Exposure ◽  
Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

Sign in / Sign up

Export Citation Format

Share Document