You learned how to pre-process your data, the basics of hierarchical clustering and the distance metrics and linkage methods it works on along with its usage in R. You also know how hierarchical clustering differs from the k-means algorithm. Well done! But there's always much more to learn...
sequence represents the center of a one-member cluster. It is run until convergence. Centers found in this batch are added to the main mean shift instance. Some of the centers found in the reservoir batch may be merged with some of the centers found so far; others are new centers. To f...
Alignment-free methods are more efficient — and could be more accurate — than alignment algorithms [30, 31]. For this reason, we utilize a machine-learning-based, alignment-free tool called Identity for predicting identity scores much faster than the Needleman-Wunsch global alignment algorithm (...
The actual clustering is done by the function hclust_fast, which supports four methods for defining a cluster distance from individual distances (see fastcluster.h for a description of the methods):int* merge = new int[2*(n-1)]; double* height = new double[n-1]; hclust_fast(n, dist...
[3] R.methodsS3_1.8.0 tidyr_1.0.2 [5] ggplot2_3.3.0 bit64_0.9-7 [7] irlba_2.3.3 multcomp_1.4-12 [9] R.utils_2.9.2 data.table_1.12.8 [11] RCurl_1.98-1.1 TH.data_1.0-10 [13] RSQLite_2.2.0 bit_1.1-15.2 [15] httpuv_1.5.2 assertthat_0.2.1 ...