ziganluo1997's Stars
StatguyUser/TextFeatureSelection
Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models
CLaraRR/autoencoder_practice
AutoEncoder implements by keras. Including AE, DAE, DAE_CNN, VAE, VAE_CNN, CVAE, Sparse AE, Stacked DAE.
kellenf/TSP_collection
TSP算法全复现：遗传(GA)、粒子群(PSO)、模拟退火(SA)、禁忌搜索(ST)、蚁群算法(ACO)、自自组织神经网络(SOM)
src-d/kmcuda
Large scale K-means and K-nn implementation on NVIDIA GPU / CUDA
subhadarship/kmeans_pytorch
kmeans using PyTorch
gugarosa/opytimizer
🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
ckeshava/lbpso-text-clustering
xiaopeng-liao/DEC_pytorch
piiswrong/dec
zhoushengisnoob/DeepClustering
Methods and Implements of Deep Clustering
betashort/DeepClusterings
Deepayan137/DeepClustering
A pytorch implementation of the paper Unsupervised Deep Embedding for Clustering Analysis.
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
jaumpedro214/Textual-Clustering-Doc2Vec-KMeans
This repository contains a step-by-step guide to use Doc2Vec vetorization process with Gensim Library and execute Clustering with KMeans
amazon-science/sccl
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021
rajeshmore1/Self-Project-K-means-clustering-and-Hierarchical-clustering
Unsupervised Learning.For this clustering project on text, you will use a dataset named 20newsgroup. This is available in sklearn.datasets. You can use the code given below in the code cells to fetch the data. Next you need to run a TFIDFVectorizer on the sentences to obtain a document-word sparse matrix. Use this array as your X . Once you have got your array, you can apply different clustering techniques such as K-Means clustering and Hierarchical clustering to obtain meaningful clusters. Check if these clusters seem relevant and well separated. Finally you can use dimensionality reduction technqiues such as PCA or t-SNE(you can read about it and use it straight away) to come up with two dimensional visualization of these clusters.
Nagesh-Cheripally/Venmo-Transaction-Clustering
Text analytics, User spending profile analytics, Emoji analytics, text dictionary, user’s spending over her lifetime
imane97-NLP/Document-clustering-in-The-context-of-Big-Data
Text clustering is a process that involves Natural Language Processing (NLP) and the use of a clustering algorithm. This method of finding groups in unstructured texts can be applied in many different segments, such as feedback analysis, research segmentation, etc.
TarunAggrawl/Model-Based-Clustering
This is a improved short text clustering model from state of the art algorithm
kypexin/text-clustering
Short sentences K-Means clustering
sujitjean/Clsutering-__-Kmeans-Allagglomerative-hierarchical-clustering
step 1: Choose any vectorizer (data matrix) that you have worked in any of the assignments, and got the best AUC value. step 2: Choose any of the feature selection/reduction algorithms ex: selectkbest features, pretrained word vectors, model based feature selection etc and reduce the number of features to 5k features. step 3: Apply all three kmeans, Agglomerative clustering, DBSCAN K-Means Clustering: ● Find the best ‘k’ using the elbow-knee method (plot k vs inertia_) Agglomerative Clustering: ● Apply agglomerative algorithm and try a different number of clusters like 2,5 etc. ● As this is very computationally expensive, take 5k datapoints only to perform hierarchical clustering because they do take a considerable amount of time to run. DBSCAN Clustering: ● Find the best ‘eps’ using the elbow-knee method. ● Take 5k datapoints only. step 4: Summarize each cluster by manually observing few points from each cluster. step 5: You need to plot the word cloud with essay text for each cluster for each of algorithms mentioned in step 3.
deyanarajib/DM_Hybrid-Reduction-Dimension-on-Clustering-Text-of-English-Hadith-Translation
Text clustering on English Hadith Translation using Hybrid Feature Extraction
deyanarajib/DM_Document-Clustering-by-Adding-Metadata-Using-the-COATES-Algorithm
Improve Text Clustering by Adding Side Information using COATES Algorithm
kennedykwangari/Natural-Language-Processing-with-Python
This repository contains data sets and code snippets on how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python!.
pokarats/gsdmm
Gibbs Sampling Dirichlet Multinomial Model (GSDMM) for Short-Text Clustering
kitaharatomoyo/ShortTextClustering
a complement of the paper"Short Text Clustering via Convolutional Neural Networks"
Kagigz/french-text-clustering
K-means clustering of French text using the CamemBERT model.
fw192020/clairvoyant_clustering
Analyzing text and clustering companies with NLP (SpaCy and CorEx) and unsupervised machine learning (K-Means and PCA)
hasitha087/LSTMtextClassifier
LSTM based text classifier using GloVe vector embedding. Model training runs on Azure Machine Learning workspace as an Experiment using compute cluster
heatherlogan/email-transfer-learning
Masters project using BERT + XLNET for text classification, sentiment analysis and clustering/topic modelling,