7 skills found
shubhamjha97 / Hierarchical ClusteringA Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.
WarrenWeckesser / HeatmapclusterA python library for generating a clustered heatmap with dendrograms.
hyunsooseol / SnowClusterThis module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.
pentalibra / GgclusterTools for creating cluster plots, tree plots and dendrograms using ggplot in [R]
luisgarzac / Data Science Course Udemy Frogames Juan Gabriel GomilaData Science course taught by Juan Gabriel Gomila. Part 1 - Installing Python and packages needed for data science, machine learning, and data visualization Part 2 - Historical evolution of predictive analytics and machine learning Part 3 - Pre-processing and data cleaning Part 4 - Data handling and data wrangling, operations with datasets and most famous probability distributions Part 5 - Review of basic statistics, confidence intervals, hypothesis tests, correlation,... Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and treatment of outliers. Part 7 - Classification with logistic regression, maximum likelihood estimation, cross validation, K-fold cross validation, ROC curves Part 8 - Clustering, K-means, K-medoids, dendrograms and hierarchical clustering, elbow technique and silhouette analysis Part 9 - Classification with trees, random forests, pruning techniques, entropy, information maximization Part 10 - Support Vector Machines for Classification and Regression Issues, Nonlinear Kernels, Face Recognition (How CSI Works) Part 11 - K Nearest Neighbors, Majority Decision, Programming Machine Learning Algorithms vs Python Libraries Part 12 - Principal Component Analysis, Dimension Reduction, LDA Part 13 - Deep learning, Reinforcement Learning, Artificial and convolutional neural networks and Tensor Flow
avogogias / MLCutA visualization support tool for advanced hierarchical clustering analysis. MLCut allows cutting dendrograms at multiple heights/levels. In other words, it allows to set multiple local similarity thresholds in potentially large dendrograms. It uses two coordinated views, one for the dentrogram (radial layout), and another for the original multidimensional data (parallel coordinates). The purpose is to add flexibility and enforce transparency in the process of selecting branches that correspond to the different clusters, while enabling the discovery of visual patterns in the original data.
cran / DynamicTreeCut:exclamation: This is a read-only mirror of the CRAN R package repository. dynamicTreeCut — Methods for Detection of Clusters in Hierarchical Clustering Dendrograms. Homepage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting/
