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Butler UniversityDigital Commons @ Butler University Undergraduate Honors Thesis CollectionUndergraduate Honors Thesis Collection2020ModelBased Cluster Analysis of Indiana Social Security Beneficiary

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How to fill out model-based cluster analysis of

01

Choose a suitable model-based clustering method such as K-means, Gaussian Mixture Models, or Hierarchical Clustering.

02

Select the number of clusters based on the dataset and the purpose of analysis.

03

Preprocess the data by handling missing values, scaling or normalizing the features, and encoding categorical variables.

04

Fit the chosen model-based clustering algorithm to the preprocessed data.

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Evaluate the cluster analysis results using metrics such as silhouette score or inertia to assess the quality of the clustering.

06

Interpret the clusters by examining the characteristics of data points within each cluster and understanding the underlying patterns.

Who needs model-based cluster analysis of?

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Researchers looking to segment their data into homogenous groups based on similarities and patterns.

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Marketers aiming to target specific customer segments for personalized marketing strategies.

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Data scientists seeking to uncover hidden patterns and structures in large datasets for better decision-making.

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What is model-based cluster analysis of?

Model-based cluster analysis is a statistical technique used to identify groups of similar objects in a dataset.

Who is required to file model-based cluster analysis of?

Researchers, data scientists, and analysts who are working with large datasets may be required to perform model-based cluster analysis.

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Model-based cluster analysis can be completed using software programs such as R, SAS, or SPSS.

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The purpose of model-based cluster analysis is to discover hidden patterns or groupings in the data that may not be immediately apparent.

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The results of the cluster analysis, including the identified clusters and the characteristics that define each cluster, must be reported.

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