International Journal of Advanced Computing, Vol.39, Special Issue.8 171 ISSN: 2044 2433

Evidence Identical Over Inquiry Results from Various Network Databases

Abstract—In a Network database scenario, most state-ofthe-art evidence identical methods such as SVM, OSVM, PEBL, and Christen are efficient in IR systems. But such methods require huge training data sets for pre learning. Earlier to address this problem Unsupervised Duplicate Detection (UDD) a inquiry-dependent evidence identical method was developed. For a given inquiry, it can effectively identify duplicates from the inquiry results of various Network databases. Non duplicate evidences from the same source can be used as training examples. Starting from a non duplicate set, UDD uses two cooperating classifiers, a weighted component similarity summing classifier and an SVM classifier that iteratively identifies duplicates in the inquiry results from various Network databases. For String Similarity calculation UDD uses any kind of similarity calculation method. We propose to use a faster better string similarity calculation (Sim String using SWIG) for optimizing the performance of UDD.