Text Clustering

Text Clustering is the process of grouping a set of documents or texts into clusters based on their content similarity. It is a common technique used in Natural Language Processing (NLP) and Information Retrieval (IR) to organize and structure large volumes of textual data.




  • Text clustering relies on similarity measures to quantify the similarity between documents. Common similarity metrics include cosine similarity, Jaccard coefficient, and Euclidean distance.
  • Unsupervised Learning: Text clustering is typically an Unsupervised Learning task, meaning that it does not require labeled training data. Instead, it groups documents based on their inherent similarities without predefined categories.
  • Before clustering, text data often undergoes preprocessing steps such as Tokenization, Stemming or Lemmatizing, Stop Words removal, and vectorization to prepare it for clustering algorithms.
  • Text clustering is often evaluated using metrics such as silhouette score, homogeneity, completeness, and Rand index, which assess the coherence and separation of the resulting clusters.