Replication Data for: Cluster analysis in practice: Dealing with outliers in managerial research
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explains that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Conclusion: we concluded that researchers need to have ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["cluster analysis","DBSCAN","fuzzy clustering","outliers","tutorial-articles"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explains that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Conclusion: we concluded that researchers need to have ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explains that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Conclusion: we concluded that researchers need to have 
cluster analysis; DBSCAN; fuzzy clustering; outliers; tutorial-articles