Self-Organizing Map for Multi-view Text Clustering

Text document clustering represents a key task in machine learning, which partitions a specific documents’ collection into clusters of related documents. To this end, a pre-processing step is carried to represent text in a structured form. However, text depicts several aspects, which a single representation cannot capture. Therefore, multi-view clustering present an efficient solution to exploit and integrate the information captured from different representations or views. However, the existing methods are limited to represent views using terms frequencies based representations which lead to losing valuable information and fails to capture the semantic aspect of text. To deal with these issues, we propose a new method for multi-view text clustering that exploits different representations of text. The proposed method explores the use of Self-Organizing Map to the problem of unsupervised clustering of texts by taking into account simultaneously several views, that are obtained from textual data. Experiments are performed to demonstrate the improvement of clustering results compared to the existing methods.