An Empirical Study of Clustering Algorithms to extract Knowledge from PubMed Articles

Abstract

Extraction of useful information from biomedical literature is one of the thrust for the world nowadays due to availability of almost articles on the web in electronic form. Information retrieval (IR) from biomedical literature is finding useful patterns from the unstructured text corpus that satisfies information. In this paper intelligent text analysis is carried out on PubMed articles related to influenza virus. In this context, various algorithms are discussed to reveal the information from PubMed articles, like year wise count of articles containing influenza virus related terms (viz. H1N1, H5N1, and H7N1 etc.), countries with their publication count, which tells about the outbreaks of the diseases in these countries. The articles may be grouped by searching the keyword “influenza virus strain” pattern with the help of regular expressions. Automatic text categorization is another challenging issue for text mining. We applied k-means, fuzzy C-means, and fuzzy C-shell algorithm for automatic categorization of text articles. The association between words based on their co-occurrence is computed which further helps to categorize the documents based on their co-occurrences. The basic k-means clustering algorithm is first applied to cluster the documents, and then to handle the fuzzy nature of words which may belong to more than one cluster, fuzzy c-means clustering is applied to form more accurate clusters. As Fuzzy c-means method clusters the documents which are in linear spaces but not in the circle, spherical, or ellipsoidal spaces. A new method is proposed here, which considers the clusters of documents in the radius of the circle.