A novel fuzzy k-means latent semantic analysis (FKLSA) approach for topic modeling over medical and health text corpora

Medical and health text documents pose a challenge for data handling and retrieving the relevant and meaningful documents. Automatically retrieval of significant knowledge with a better understanding of medical and health documents is a challenging task. One popular approach for thematically understand the medical and health text documents and finding the topics from these documents is topic modeling. In this research, we propose a novel topic modeling approach Fuzzy k-means latent semantic analysis (FKLSA) by using the fuzzy clustering. Our method generates local and global term frequencies through the bag of words (BOW) model. Principal component analysis is used for removing high dimensionality negative impact on global term weighting. Previous work shows that in medical and health documents redundancy issue has a negative impact on the quality of text mining. Therefore, the main achievement of FKLSA is the handling of the redundancy issue in medical and text documents and discover semantically more precise topics. FKLSA is socially utilized for finding the themes from medical and health text corpus. These topics are further used for text classification and clustering tasks in text mining. Experimental results show that FKLSA performs better than LDA and RedLDA for redundant corpora. FKLSA’s time performance is also stable with an increase in number of topics and thus better than LDA and LSA on a big twitter heath dataset. Quantitative evaluations of the real-world dataset for health and medical documents show that FKLSA gives a higher performance as compared to state-of-the-art topic models like Latent Dirichlet allocation and Latent semantic analysis.