Commit 43489a9b authored by dmattek's avatar dmattek

Modified help text

parent f173370e
......@@ -9,13 +9,13 @@ helpText.clHier = c(alertNAsPresentClDTW = paste0("NAs (still) present. DTW cann
alertNAsPresentCl = paste0("NAs (still) present, caution recommended. If interpolation is active in the left panel, ",
"missing data can be due to removed outlier time points."),
alLearnMore = paste0("<p><a href=\"https://en.wikipedia.org/wiki/Hierarchical_clustering\" target=\"_blank\" title=\"External link\">Agglomerative hierarchical clustering</a> ",
"initially assumes that all time series are forming their own clusters. It then grows a clustering dendrogram thanks to 2 inputs:<p>",
"First, a <b>dissimilarity matrix</b> between all pairs ",
"initially assumes that all time series are forming their own clusters. It then grows a clustering dendrogram using two inputs:<p>",
"A <b>dissimilarity matrix</b> between all pairs ",
"of time series is calculated with one of the metrics, such as ",
"Euclidean (<a href=\"https://en.wikipedia.org/wiki/Euclidean_distance\" target=\"_blank\" title=\"External link\">L2 norm</a>) ",
"or Manhattan (<a href=\"https://en.wikipedia.org/wiki/Taxicab_geometry\" target=\"_blank\" title=\"External link\">L1 norm</a>) distance. ",
"<a href=\"https://en.wikipedia.org/wiki/Dynamic_time_warping\" target=\"_blank\" title=\"External link\">Dynamic Time Warping</a> (DTW) ",
"is another distance metric that does not only compare series point by point but also tries to align them such that shapes between the 2 series are matched. ",
"Euclidean (<a href=\"https://en.wikipedia.org/wiki/Euclidean_distance\" target=\"_blank\" title=\"External link\">L2 norm</a>), ",
"Manhattan (<a href=\"https://en.wikipedia.org/wiki/Taxicab_geometry\" target=\"_blank\" title=\"External link\">L1 norm</a>), or ",
"<a href=\"https://en.wikipedia.org/wiki/Dynamic_time_warping\" target=\"_blank\" title=\"External link\">Dynamic Time Warping</a> (DTW). ",
"Instead of comparing time series point by point, DTW tries to align and match their shapes. ",
"This makes DTW a good quantification of similarity when signals are similar but shifted in time.</p>",
"<p>In the second step, clusters are successively built and merged together. The distance between the newly formed clusters is determined by the <b>linkage criterion</b> ",
"using one of <a href=\"https://en.wikipedia.org/wiki/Hierarchical_clustering\" target=\"_blank\" title=\"External link\">linkage methods</a>.</p>"))
......
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