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Text clustering is a method of grouping similar text documents together based on their content. This technique is widely used in various applications such as topic modeling, document organization, and information retrieval. One of the most popular algorithms for text clustering is K-means.
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To demonstrate text clustering, we can use the 20 newsgroups dataset, which contains around 18,000 newsgroup posts on 20 topics. For simplicity, we can select a subset of 4 topics, resulting in around 3,400 documents1.
import numpy as npfrom sklearn.datasets import fetch_20newsgroupscategories = ["alt.atheism", "talk.religion.misc", "comp.graphics", "sci.space"]dataset = fetch_20newsgroups(remove=("headers", "footers", "quotes"), subset="all", categories=categories, shuffle=True, random_state=42)labels = dataset.targetunique_labels, category_sizes = np.unique(labels, return_counts=True)true_k = unique_labels.shape[0]print(f"{len(dataset.data)} documents - {true_k} categories") Clustering text documents using k-means - scikit-learn
Clustering text documents using k-means# This is an example showing how the scikit-learn API can be used to cluster documents by topics using a Bag of Words approach. Two algorithms are demonstrated, namely KMeans and its more …
Text Clustering with K-Means - Medium
Dec 17, 2019 · The idea behind this article is to understand how to represent text data (in this case national anthems) so that it can be used as input for the K …
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Clustering Text Documents using K-Means in Scikit …
Feb 3, 2025 · The article explains how to perform text document clustering for sarcasm detection in headlines using the K-Means algorithm and Scikit-Learn in Python.
Text clustering with K-means and tf-idf - Medium
Aug 5, 2018 · Clustering Text Data with K-Means and Visualizing with t-SNE In NLP, analyzing and grouping text data into meaningful clusters is a vital task. …
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How to Cluster Documents Using Word2Vec and K-means
Jan 18, 2021 · Learn how to cluster documents using Word2Vec. In this tutorial, you’ll train a Word2Vec model, generate word embeddings, and use K-means to create groups of news …
A Friendly Introduction to Text Clustering | by …
Mar 25, 2020 · K-means assigns k random points in the vector space as initial, virtual means of the k clusters. It then assigns each data point to the nearest cluster mean. Next, the actual mean of each cluster is recalculated. Based on …
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Text classification using k-means | by dennis ndungu
Sep 16, 2018 · In this post i will demonstrate on how to use k-means algorithm to cluster headlines into different categories using python. The data set used is obtained from kaggle data sets and the link is...
Text Documents Clustering using K-Means Algorithm
Jan 26, 2013 · The K-Means algorithm aims to partition a set of objects, based on their attributes/features, into k clusters, where k is a predefined or user-defined constant. The main idea is to define k centroids, one for each cluster.
Optimizing Text Summarization with Gensim and K-means …
Text summarization: The process of reducing the size of large text documents while preserving their essential information. Topic modeling: A technique for discovering hidden topics in a …
algorithm - k-means for text clustering - Stack Overflow
Nov 3, 2016 · I'm trying to implement k-means for text clustering, specifically English sentences. So far I'm at the point where I have a term frequency matrix for each document (sentence). I'm …
Text Clustering using K-Means with Sklearn
K-Means clustering is a machine learning method that helps to organize data into groups based on how similar or different they are. The goal is to divide the data into K clusters, with each data point belonging to the cluster that has the …
K-Means Clustering in Python Using SciPy: A Step-by-Step Guide
Feb 17, 2025 · K-Means Clustering with SciPy in Python What is K-Means Clustering? K-Means is an unsupervised learning algorithm used to group data into K clusters. It works by: Choosing K …
Clustering Text Data with K-Means and Visualizing with t-SNE
Oct 11, 2024 · In NLP, analyzing and grouping text data into meaningful clusters is a vital task. Clustering helps us discover hidden patterns or structures in large datasets, such as grouping …
Clustering text documents using k-means - scikit-learn
Two algorithms are demoed: ordinary k-means and its more scalable cousin minibatch k-means. Additionally, latent semantic analysis can also be used to reduce dimensionality and discover …
Clustering Text Data: A Practical Guide - Flare Compare
Apr 20, 2021 · It is a simple and effective method to group text data into clusters, based on their similarity. The idea behind K-Means clustering is to divide the data into K clusters by …
Clustering text documents using k-means - scikit-learn
Clustering text documents using k-means¶ This is an example showing how the scikit-learn API can be used to cluster documents by topics using a Bag of Words approach. Two algorithms …
(PDF) Text Clustering using K-MEAN - ResearchGate
Aug 10, 2021 · The results show that a clustering strategy paired with a K-mean clustering algorithm with TF-IDF features has already been used. The dataset is available on GitHub...
Document Clustering using K Means - OpenGenus IQ
In this article, we use ideas from TF IDF and similarity metrics to use K Means clustering algorithm to cluster documents.
Using K-Means Clustering to Categorize Text Data - Medium
Aug 26, 2021 · How to use unsupervised learning to categorize text entry fields in a dataset. In this post, I will share an example of using K-Means Clustering to better categorize text fields in …
Clustering text documents using k-means — scikit-learn 0.11-git ...
This is an example showing how the scikit-learn can be used to cluster documents by topics using a bag-of-words approach. This example uses a scipy.sparse matrix to store the features …
Text Clustering with K-Means in an automated Approach
May 22, 2021 · When it comes to unsupervised learning techniques on text data like clustering we all are aware of its related techniques/algorithms, but in this tutorial I wanted to show you how …
K-Means Clustering: A Tool for English Language Teaching
3 days ago · To read the full-text of this research, you can request a copy directly from the authors. ... Ultimately, by combining big data information fusion with the K-means clustering …
Randomized Sketches for Clustering: Fast and Optimal Kernel …
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