Clustering Documents using the Document to Vector Model for Dimensionality Reduction
Robert-George Radu, Iulia-Maria Rădulescu, Ciprian‐Octavian Truică, Elena‐Simona Apostol, Mariana Mocanu
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.
Topics & Concepts
Cluster analysisComputer scienceDimensionality reductionCurse of dimensionalityDocument clusteringDBSCANVector space modelArtificial intelligenceWord (group theory)Vector spacePattern recognition (psychology)Data miningCanopy clustering algorithmCorrelation clusteringMathematicsGeometryImage Retrieval and Classification TechniquesAdvanced Clustering Algorithms ResearchWeb Data Mining and Analysis