Commit 0f6fcc9d authored by majpark21's avatar majpark21

Update learn more hierarchical

parent 425d1f5e
...@@ -8,14 +8,16 @@ helpText.clHier = c(alertNAsPresentClDTW = paste0("NAs (still) present. DTW cann ...@@ -8,14 +8,16 @@ helpText.clHier = c(alertNAsPresentClDTW = paste0("NAs (still) present. DTW cann
"If interpolation is active in the left panel, missing data can be due to removed outlier time points."), "If interpolation is active in the left panel, missing data can be due to removed outlier time points."),
alertNAsPresentCl = paste0("NAs (still) present, caution recommended. If interpolation is active in the left panel, ", 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."), "missing data can be due to removed outlier time points."),
alLearnMore = paste0("<p>Clustering consists of two steps. First, a distance between all pairs ", 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 ",
"of time series is calculated with one of the metrics, such as ", "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>) ", "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. ", "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) ", "<a href=\"https://en.wikipedia.org/wiki/Dynamic_time_warping\" target=\"_blank\" title=\"External link\">Dynamic Time Warping</a> (DTW) ",
"also quantifies similarity between two time series but ", "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. ",
"contrary to other distance measures it accounts for the order of time points.</p>", "This makes DTW a good quantification of similarity when signals are similar but shifted in time.</p>",
"<p>In the second step, distances are arranged hierarchicaly and visualised as a dendrogram ", "<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>")) "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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