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A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart and has been identified as the prototype of charts. [1]
Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable. These clustered groups can be differentiated using color. For example; comparison of values, such as sales performance for several persons or businesses in a single time period. Variable-width bar chart relating:
Line charts — Accepts up to six datasets. (updated 30 August 2023) Vertical bar charts (column charts) — Accepts up to six datasets. Toggle between clustered and stacked charts; user can adjust "Yfloor"—the Y level (usually=0) from which columns rise or fall; user chooses to keep or ignore negative input values. (updated 27 August 2023)
Every cluster graph is a block graph, a cograph, and a claw-free graph. [1] Every maximal independent set in a cluster graph chooses a single vertex from each cluster, so the size of such a set always equals the number of clusters; because all maximal independent sets have the same size, cluster graphs are well-covered.
bar charts: to visualize one or more series of data; line charts: to track changes in several dependent data sets over a period of time; sparklines: to show the trend in a single data set; scorecards: to monitor KPIs and trends; use of legends anytime more than one color or shape is present on a graph
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For a clustering example, suppose that five taxa (to ) have been clustered by UPGMA based on a matrix of genetic distances.The hierarchical clustering dendrogram would show a column of five nodes representing the initial data (here individual taxa), and the remaining nodes represent the clusters to which the data belong, with the arrows representing the distance (dissimilarity).
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]