Clustering Documents using the Document to Vector Model for Dimensionality Reduction

Abstract

The TF-IDF model is the most common way of representing documents in the vector space. However, its results are highly dimensional, posing problems to the classic clustering algorithms due to the curse of dimensionality. Recent word embeddings based techniques can reduce the documents representations dimensionality while also preserving the semantic relationships between words. In this paper, we analyze the accuracy of four different classical clustering algorithms (K-Means, Spherical K-Means, LDA, and DBSCAN) in combination with the Document to Vector model.