HFRECCA for clustering of text data from travel guide articles

Text clustering is advantageous for extraction of text data from web applications such as e-news papers, collection of research papers, blogs, news feeds at social networks, etc. This paper presents a text clustering Hierarchical Fuzzy Relational Eigenvector Centrality-based Clustering Algorithm (HFRECCA). The algorithm is a combination of fuzzy clustering, divisive hierarchical clustering and page rank algorithm. Travel guide articles are pre-processed to remove stop words and stemming. Then, similarity matrix is generated using word distance computation. In HFRECCA, divisive hierarchical clustering algorithm is applied where it uses Fuzzy Relational Eigenvector Centrality-based Clustering Algorithm (FRECCA) as sub routine algorithm. FRECCA outputs cluster membership values on the basis of page rank score using page rank algorithm and generate clusters according to it. HFRECCA has features of hierarchical clustering as well as fuzzy clustering as it creates hierarchy of clusters and an object can belong to multiple clusters. Structure of information resides in text documents is hierarchical hence HFRECCA is useful for clustering of data from natural language documents.