A Parametric Study of Partitioning and Density Based Clustering Techniques for Boxplot Generation

The Boxplot uses five point summary of data to display information. K-Means and DBSCAN clustering methods are used to improve the Boxplots. Both the above methods have parameters which affect the clusters formed by the algorithms. The results obtained by varying these parameters i.e. K in K-Means and Eps and MinPts in DBSCAN clustering methods are studied in this paper. The Boxplots obtained by varying the above mentioned parameters are plotted and the effects of varying the parameters have been studied. A comparative figure of traditional Boxplot and Boxplots generated using K-Means and DBSCAN clustering has also been plotted. The comparisons and differences have been discussed between the above mentioned Boxplots.