Fish Schooling Algorithm and Hash Based Indexing for Text Document Retrieval
DOI:
https://doi.org/10.26438/ijcse/v9i11.2428Keywords:
Clustering, Genetic Algorithm, Text Mining, Pattern FeatureAbstract
Publishers are getting content frequently as demand of publication increases day by day. To resolve an issue of identifying the research paper class as per content this work proposed a hybrid model. Features were select by the fish schooling genetic algorithm and indexing was provide by hash structure. In order to maintain the privacy of the user and server data model work on key based searching of relevant document. Each document has set of keywords and each keyword has its own unique key. So user query pass as set of unique keys and searching of cluster document was done by matching keys with hash index. Experiment was done on real dataset having set of document from different field of publication. Result shows that proposed model FSGA has increases the result outcome by fetching more relevant text documents as per user query.
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