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:: Course SummaryThis training session covers a powerful multivariate technique, cluster analysis, that comprises a diverse collection of techniques that can be used to classify objects (e.g. individuals, countries, species, cells, genes, etc).
Cluster analysis is an exploratory tool, with the ability to form homogeneous groups of individuals or objects.:: Learning ObjectivesUpon completion of this course, participants will be able to:
Understand how to calculate the distance between objects based on the nature of the classification variables
Differentiate between the different classification techniques available
Select an appropriate clustering technique based on the study objective and the type of classification variables selected
Graphically represent results and interpret them (dendrograms)
Determine the number of clusters to retain
Validate and interpret the groups formed
Consider the limitations and difficulties associated with cluster analysis:: Target AudienceThis applied training session in statistics is intended for all scientific staff who collect large datasets and who wish to graphically summarize them as well as identify groups of objects or individuals with similar characteristics.:: PrerequisiteThis workshop introduces the important concepts in statistics and data analysis. It assumes that participants have no previous knowledge of statistics or that they have not used it for a long time.:: Notes and Other InformationIf you are interested in:
- acquiring or furthering your skills in multivariate data analysis methods
- putting the theory of multivariate analysis to practice with a wide array
of case studies
- and doing so with your very own software of choice
then our our summer
school on multivariate data analysis is what you are looking for!
All our training sessions are available on-site
Contact us to learn more | | |
:: Topics Covered
- Introduction: Context of Use, Objective, Terminology
- Determining the Distance Between Objects
- Hierarchical Methods
- Modeling Techniques: Ward, etc.
- Optimization Methods
- Other Methods
- Use and Interpretation of Classes
- Summary
- References
:: Course ContentCluster analysis comprises a series of methods for determining natural groupings in multivariate data. It is designed to answer the following question: Given a dataset with one or more characteristics (i.e. variables), how can I classify the data into clusters so that they are as similar as possible within each cluster and as different as possible between them?
To answer this answer, the objectives of the classification must be defined.
As a first step in the classification procedure, techniques for calculating the distance between objects or observations are illustrated, stressing the importance of the types of variables in the selection of the appropriate technique.
Second, the different clustering methods are presented and the advantages and disadvantages of each are discussed: hierarchical methods, modeling techniques, optimization methods. For each method, the context of use, characteristics, graphical representation and interpretation are outlined.
Recent alternative methods are also reviewed: fuzzy clustering and nonparametric distributions.
Throughout the training, examples and case studies will be provided to ensure a concrete application of the concepts presented.
Software options and limitations will also be discussed.
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