Text Data Clustering Workflow: Preprocessing, Vectorization, Dimensionality Reduction & Evaluation…
📰 Medium · Data Science
Learn a step-by-step text data clustering workflow to improve your model with silhouette, elbow, and inertia metrics
Action Steps
- Preprocess text data using techniques such as tokenization and stopword removal
- Vectorize preprocessed text data using methods like TF-IDF or word embeddings
- Apply dimensionality reduction techniques such as PCA or t-SNE to reduce vector space
- Evaluate clustering models using metrics like silhouette, elbow, and inertia to determine optimal cluster numbers
Who Needs to Know This
Data scientists and analysts can benefit from this workflow to organize and derive meaningful insights from complex text data
Key Insight
💡 Text data clustering workflow involves preprocessing, vectorization, dimensionality reduction, and evaluation to derive meaningful insights from complex text data
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📊 Improve your text data clustering model with these 4 steps: preprocessing, vectorization, dimensionality reduction, and evaluation 📈
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