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Source code has been (very) slightly modified into fully object-oriented matplotlib interface. A pseudo-random generator is used with a constant seed to ensure reproducibility when updating in the futre. The original shebang was also removed. The matplotlib (mpl) version is 1.5.3, with Python 2.7 and numpy 1.10
A scatter plot, also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram, [2] is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are coded (color/shape/size), one additional variable can be displayed.
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
A sina plot is a type of diagram in which numerical data are depicted by points distributed in such a way that the width of the point distribution is proportional to the kernel density. [ 1 ] [ 2 ] Sina plots are similar to violin plots , but while violin plots depict kernel density, sina plots depict the points themselves.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
Scagnostics (scatterplot diagnostics) is a series of measures that characterize certain properties of a point cloud in a scatter plot. The term and idea was coined by John Tukey and Paul Tukey , though they didn't publish it; later it was elaborated by Wilkinson, Anand, and Grossman.
DBSCAN optimizes the following loss function: [10] For any possible clustering = {, …,} out of the set of all clusterings , it minimizes the number of clusters under the condition that every pair of points in a cluster is density-reachable, which corresponds to the original two properties "maximality" and "connectivity" of a cluster: [1]
In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use.