String Transformation for Effective Search

String transformation can be considered as a problem in natural language processing such as data mining,
information retrieval, bioinformatics, medical science etc. If we simply consider the medical system there is
great need of string transformations in the system. Not only common user but the experts also need the help of
string suggestions for accurate results. There are systems which uses different methods of string
transformation and generation for giving better results. But these results are accurate and efficient for only
small scale datasets. Here a method is proposed which not only give accurate results but also efficient. Thus
different string transformation methods are used and queries are reformulated for getting accurate as well as
efficient results. The approach includes a model which is used to find the different strings from the databases
which match the input string or query on the basis of parameter estimation. An algorithm is used to find the
top K matching candidates. The proposed method is used for correction of spelling errors in queries and
reformulates the query for better search results.
According to experimental results on large scale datasets the proposed method is accurate and efficient on
different string transformation methods.