Cluster Analysis: An Introduction in Psychological Contexts

In the market survey, companies want to know where the shopper is most likely to purchase their products. In medicine, geneticists study genomic and gene expression to determine the possibility of Alzheimer's disease, cancer survival, or congenital onset with amyotrophic lateral sclerosis (ALS) or Lou Gehrig disease. In psychiatry, physicians examined the patient response patterns of depression questionnaires to distinguish between patients with chronic stable depression and patients with specific conditions or acute depression.

Introduction: Interaction transcripts can be examined using a variety of analytical methods such as session analysis (CA), interactive sociolinguistics, politeness theory, critical discourse analysis, discourse psychology. Based on the focus of the survey and the level of detail you want to extract from the data, researchers decide which analysis framework to use (Stubbe, Lane, Hilder, Vine, Vine, Marra, Holmes, and Weatherall, 2009).

Clustering and Disassembly Cluster analysis methods can be used to identify groups of documents (eg, owners of new cars) and to identify groups similar to input text. This type of analysis is also very useful from the viewpoint of market research, such as owners of new cars. Factor analysis and principal component classification and classification analysis (word or document analysis) can also be used.

Gray clustering method Clustering analysis is to use mathematical clustering to quantitatively determine relationships between objects, which is a kind of multivariate analysis and gray clustering analysis method introduces a whitening function It is a common method. Indicator class object, according to some gray summary, we propose some green gradation Green function to be the basis of the new clustering method.

Cluster analysis groups similar objects into mutually exclusive different subsets called clusters. The goal is to group data units or variables into clusters so that the elements in the cluster have highly natural associations while the clusters remain relatively different from each other. Mulvey and Crowder (1979) proposed and tested an efficient optimization algorithm for clustering homologous data. Punj and Stewart (1983) reviewed the application of clustering analysis in marketing issues. They proposed an alternative approach to cluster analysis to evaluate their performance characteristics. They also discussed issues related to the use and validation of cluster analysis methods. Ketchen and Shook (1996) studied the application of cluster analysis in strategic management research. Their papers document the application of cluster analysis in strategic management research.