18 skills found
issaz / Signature Regime DetectionCode accompanying the paper "Pathwise methods for non-parametric online market regime detection and regime clustering for multidimensional and non-Markovian data"
hj-n / Steadiness CohesivenessQuality Metrics for evaluating the inter-cluster reliability of Multidimensional Projections
enfantbenidedieu / ScientisttoolsPython library for multidimensional analysis, classification - clustering analysis
ccpem / Affinity VaeSelf-supervised method for disentanglement, clustering and classification of objects in multidimensional image data
gmrukwa / DivikDivisive Intelligent K-Means algorithm (DiviK) for joint feature selection and clustering of heavily multidimensional data.
clugen / PyclugenMultidimensional cluster generation in Python
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.
zslwyuan / KMeans Emails Clustering Visualization NLPKMeans-Emails-Clustering-Visualization-NLP: KMeans is used to cluster the emails. The words in the contents of emails are tokenlized and stemmed. This project transforms the corpus into vector space using tf-idf.By multidimensional scaling, the clustering result is visualized.
clugen / CluGen.jlMultidimensional cluster generation in Julia
locklocke / Mdst DbscanR functions for clustering multidimensional spatial–temporal data
ishspsy / MKerW AIntegrating multidimensional data for clustering analysis
clugen / ClugenrMultidimensional cluster generation in R
clugen / MOCluGenMultidimensional cluster generation in MATLAB/Octave
emadof85 / Population ClusteringAnalyzing Population Data to Identify Housing Patterns Using Multidimensional Clustering and the K-means Algorithm
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.
brantburnett / Terraform Aws CouchbaseCreates a complete Couchbase cluster in AWS, with support for Multidimensional Scaling
kaenova / Malin Tubes1[Done] An implementation of Unsupervised Learning Clustering using K-Means with Multidimensional Euclidean Distance. Easily use the library I made by importing the file in ./module 📚
internaut / JGenLloydClusterGenerlized Lloyd / Linde-Buzo-Gray implementation in Java. Can be used to generate cluster points from a big amount of multidimensional vectors. Also includes a set of distance metrics (hausdorff / modified hausdorf, histogram intersection, etc.) to calculate distances between vectors.
