16 skills found
milosgajdos / GosomSelf-organizing maps in Go
enricivi / Growing Hierarchical SomSelf-Organizing Map [https://en.wikipedia.org/wiki/Self-organizing_map] is a popular method to perform cluster analysis. SOM shows two main limitations: fixed map size constraints how the data is being mapped and hierarchical relationships are not easily recognizable. Thus Growing Hierarchical SOM has been designed to overcome this issues
gtkfi / GisSOMSelf-organizing maps (SOM) and k-means clustering for analyzing geospatial data.
michelin / TorchSOMTorchSOM is a PyTorch-based library for training Self-Organizing Maps (SOMs), a model trained in an unsupervised manner, that can be used for clustering, dimensionality reduction and data visualization. It is designed to be scalable and user-friendly.
yashpandey474 / Identification Of Fake ReviewsFake review detection using machine learning and deep learning techniques such as CNNs, SOMs, K-means clustering, various supervised models and natural language processing tools such as Word2Vec & TFIDF, GloVe etc.
JRC1995 / Self Organizing MapSOM clustering on IRIS dataset
hl-public / SOMPY Robust ClusteringModification of SOMPY repo with robust K-means clustering (bootstrapped SSE elbow method)
Moh-Joshaghani / The Sales Person Problem TSP SOM Self Organizing MAPMatlab and Python implementation of the TSP using the Self-Organizing Map (SOM) clustering algorithm
HRakesh / Healthcare Data Analysis On PIMA Indian Diabetes DatabaseKnowledge Discovery in Database. * In this project we focused our analysis on applying data analysis techniques, create visualizations and interpret the models using histograms, scatter plots and many other visual plots etc. to uncover the reason for high diabetic outcome among the PIMA Indian women. * Predict whether the patient is diagnosed with diabetes based on diagnostic measurements available in the dataset. * We obtained this dataset from Kaggle and built some supervised explanatory models (classification tree and logistic regression) and predictive models (KNN); also tried Unsupervised techniques such as K-means Clustering and Kohonen's Self-Organizing Map (SOM).
kakshak07 / High Dimensional Visualization Techniques And AnalysisAnalysis involved various methods to explore and organize the data, including clustering, force-directed layouts, and a timeline for navigating through time-series data. To further analyse temporal attributes, the timeline was distorted through a force directed layout to spatially position time points according to their similarity. Sammon mapping, a variant of MDS, was used as a visualization approach for gene expression data sets along with the heat map-based strategy has been presented for visualizing the U-matrix from self-organizing maps (SOM).
noortitan / PvkSOMPerovskite solar cells degradation data clustering using self-organizing map (SOM)
fruitzzx / Printed Digits ClassificationThis project presents a hybrid algorithm for training RBF network based on K-means and SOM. The algorithm consists of a proposed clustering algorithm to position the RBF center and givens least squares to estimate the weights. The aim of this experiment is to recognize printed digits (1-4) using the hybrid model. In the meanwhile, KNN and MLP with Scaled Conjugate Gradient will be implemented in order to show the comparative of different models according to the experiments.
JeffBorwey / GraphClusteringGraphClustering is a C# GUI for data mining and clustering research. In particular, this implements the graph based resilience measure Vertex Attack Tolerance (VAT) and the adapted clustering algorithm Hierarchical VAT Clustering (hVATClust). The NetMining library provides many other common clustering algorithms (K-Means, SOM, Girvan-Newman, etc.), Several ADTs (Quadtree, Heap, DisjointSet), Dimensionality Reduction, Data Generation, and Internal(Dunn, Silhouette, Davies–Bouldin) and External Clustering evaluation.
AntonBryansky / Clustering MatlabI use c-means, k-means, and SOM NN for clusterization
SamirArthur / Is Word2Vec Really Meaningful SemanticallyWhat does English language look like if we print it on a 2D map? here is a clustering of 300k words on top on SOM applied to Word2Vec embeddings
asifehmad / Machine Learning Lecture Slides ES 442Complete lecture slides for Machine Learning (ES-442) at GIK Institute, Fall 2025. Covers Supervised Learning (Decision Trees, SVM, Neural Networks), Unsupervised Learning (Clustering, SOM), and Reinforcement Learning (MDPs, Q-Learning, Deep RL).
