Application of Table based K Nearest Neighbor for Index Optimization

This article proposes the modified KNN (K Nearest Neighbor)algorithm which receives a table as its input data and is applied tothe index optimization. The motivations of this research are thesuccessful results from applying the table based algorithms to thetext categorizations in previous works and the index optimization isable to be viewed into a classification task where each word isclassified into expansion, inclusion, and removal. In the proposedsystem, each word in the given text is classified into one of thethree categories by the proposed KNN algorithm, associates words areadded to ones which are classified into expansion, and ones whichare classified into inclusion are kept by themselves without addingany word. The proposed KNN version is empirically validated as thebetter approach in deciding the importance level of words in newsarticles and opinions. In using the table based KNN algorithm, it iseasier to trace results from categorizing words.