Abstract: Text mining works widely in the field of research techniques, which allows an individual to store text and its important terms in form of electronic document (.doc, .txt). It is difficult to remember such huge amount of text; moreover the manual approach is more time taking, unreliable and accessible to that person only. Text mining techniques optimize this approach by extracting and storing this data. Computational comparison, file read, file write are done more efficiently. With the help of Bio-Cloud, we generated more semantically similar, related and significant patterns. The give, generate and get sequence modeling is adopted. Over the other available web applications, we present our application with improved stemming, relation and average case consideration. This approach do not limit the displayed number of words as all the generated sets can be traversed with the GUI, with opted size of patterns. This method is highly applicable in bioinformatics, related information retrieval from document, sentimental analysis using social websites (Twitter and Facebook), query expansion (Google) and many more.
Keywords: Word cloud, biological pattern analysis, bioinformatics, text mining.