Enhanced Document Retrieval System Using Suffix Tree Clustering Algorithm

Most search engines in use today present the user with a single-ordered list of documents matching the search query leading to lexical ambiguity. An alternative to a single-ordered list is to cluster the search results and present a list of clusters to the user. This study implements the suffix tree clustering algorithm to optimize search. The user selects which cluster appears most relevant and the search results in that cluster are then displayed in a list under the assumption that similar documents are likely to be relevant to the same query. The proposed system clusters search results from the file system. The proposed system allows the user to issue a search query and we return the results as a set of coherent clusters. The suffix tree clustering algorithm efficiently determined documents that share common phrases. The nodes in the suffix tree define the initial cluster and to increase the number of documents in each cluster, clusters that are sufficiently similar are merged. The proposed system adopted web technologies such as hypertext markup language (HTML), and cascade styling sheet (CSS), to design the interface, while Javascript programming language was used to implement the entire system. The proposed system was implemented using PHP5 and MySQL database. The experimental results show that the suffix tree clustering algorithm can be used to cluster documents efficiently. The resulting system demonstrated an optimized search of 4.1 trillion search results of the word “Electricity” whereas a total result of 4.3 trillion was retrieved by the conventional Google Search Engine.