Unsupervised learning in latent space with a fuzzy logic guided modified BA

In this paper, a modified bat algorithm with fuzzy inference mamdani-type system is applied to the problem of document clustering in a semantic features space induced by SVD decomposition. The algorithm learns the optimal clustering of the documents as well as the optimal number of clusters in a concept space; thus, making it suitable for a large and spare dataset which occur in information retrieval system. a centroid-based solution in multidimensional space is evaluated with a silhouette index. A TF-IDF method is used to represent documents in vector space. The presented algorithm is tested on the 20 newsgroup dataset.