FOCT: Fast Overlapping Clustering for Textual data

Text clustering is used to extract specific information from textual data and even categorizes text based on topic and sentiment. Due to inherent overlapping in textual documents, overlapping clustering algorithms have become a suitable approach for text analysing. However, state-of-the-art algorithms are not fast enough to analyse a large volume of textual data within tolerable time limits. In this research, we propose our text clustering algorithm, FOCT, which is a fast overlapping extension of SOM, one of the best algorithms for clustering textual data. We apply some heuristics to extract special characteristics presented in textual data and establish a very fast overlapping clustering algorithm. We use fast methods to represent the vectors of documents, compute the similarity of documents and neurons and update the weights of neurons. In our algorithm, each document can belong to one or more neurons and this is in line with what many documents have in their essence. We analyse the efficiency of the proposed algorithm over k-means, OKM, SOM and OSOM clustering approaches and experimentally demonstrate that it runs 12 to 690 times faster, and the overlap size of FOCT clusters is closer to the overlap size of the original data. The quality of clusters is also measured by four different internal and external evaluation criteria where FOCT clusters represent up to 64% better quality.